Object re-identification during image tracking

ABSTRACT

A system includes sensors and a tracking subsystem. The subsystem tracks first and second objects in a space. Following a collision event between the first and second object, a top-view image of the first object is received from a first sensor. Based on the top-view image, a first descriptor is determined for the first object. The first descriptor is associated with an observable characteristic of the first object. If criteria are not satisfied for distinguishing the first object from the second object based on the first descriptor, a third descriptor is determined for the first object. The third descriptor is generated by an artificial neural network configured to identify objects in top-view images. The tracking subsystem uses the third descriptor to assign an identifier to the first object.

TECHNICAL FIELD

The present disclosure relates generally to object detection andtracking, and more specifically to object re-identification during imagetracking.

BACKGROUND

Identifying and tracking objects within a space poses several technicalchallenges. Existing systems use various image processing techniques toidentify objects (e.g. people). For example, these systems may identifydifferent features of a person that can be used to later identify theperson in an image. This process is computationally intensive when theimage includes several people. For example, to identify a person in animage of a busy environment, such as a store, would involve identifyingeveryone in the image and then comparing the features for a personagainst every person in the image. In addition to being computationallyintensive, this process requires a significant amount of time whichmeans that this process is not compatible with real-time applicationssuch as video streams. This problem becomes intractable when trying tosimultaneously identify and track multiple objects. In addition,existing system lacks the ability to determine a physical location foran object that is located within an image.

SUMMARY

Position tracking systems are used to track the physical positions ofpeople and/or objects in a physical space (e.g., a store). These systemstypically use a sensor (e.g., a camera) to detect the presence of aperson and/or object and a computer to determine the physical positionof the person and/or object based on signals from the sensor. In a storesetting, other types of sensors can be installed to track the movementof inventory within the store. For example, weight sensors can beinstalled on racks and shelves to determine when items have been removedfrom those racks and shelves. By tracking both the positions of personsin a store and when items have been removed from shelves, it is possiblefor the computer to determine which person in the store removed the itemand to charge that person for the item without needing to ring up theitem at a register. In other words, the person can walk into the store,take items, and leave the store without stopping for the conventionalcheckout process.

For larger physical spaces (e.g., convenience stores and grocerystores), additional sensors can be installed throughout the space totrack the position of people and/or objects as they move about thespace. For example, additional cameras can be added to track positionsin the larger space and additional weight sensors can be added to trackadditional items and shelves. Increasing the number of cameras poses atechnical challenge because each camera only provides a field of viewfor a portion of the physical space. This means that information fromeach camera needs to be processed independently to identify and trackpeople and objects within the field of view of a particular camera. Theinformation from each camera then needs to be combined and processed asa collective in order to track people and objects within the physicalspace.

The system disclosed in the present application provides a technicalsolution to the technical problems discussed above by generating arelationship between the pixels of a camera and physical locationswithin a space. The disclosed system provides several practicalapplications and technical advantages which include 1) a process forgenerating a homography that maps pixels of a sensor (e.g. a camera) tophysical locations in a global plane for a space (e.g. a room); 2) aprocess for determining a physical location for an object within a spaceusing a sensor and a homography that is associated with the sensor; 3) aprocess for handing off tracking information for an object as the objectmoves from the field of view of one sensor to the field of view ofanother sensor; 4) a process for detecting when a sensor or a rack hasmoved within a space using markers; 5) a process for detecting where aperson is interacting with a rack using a virtual curtain; 6) a processfor associating an item with a person using a predefined zone that isassociated with a rack; 7) a process for identifying and associatingitems with a non-uniform weight to a person; and 8) a process foridentifying an item that has been misplaced on a rack based on itsweight.

In one embodiment, the tracking system may be configured to generatehomographies for sensors. A homography is configured to translatebetween pixel locations in an image from a sensor (e.g. a camera) andphysical locations in a physical space. In this configuration, thetracking system determines coefficients for a homography based on thephysical location of markers in a global plane for the space and thepixel locations of the markers in an image from a sensor. Thisconfiguration will be described in more detail using FIGS. 2-7.

In one embodiment, the tracking system is configured to calibrate ashelf position within the global plane using sensors. In thisconfiguration, the tracking system periodically compares the currentshelf location of a rack to an expected shelf location for the rackusing a sensor. In the event that the current shelf location does notmatch the expected shelf location, then the tracking system uses one ormore other sensors to determine whether the rack has moved or whetherthe first sensor has moved. This configuration will be described in moredetail using FIGS. 8 and 9.

In one embodiment, the tracking system is configured to hand offtracking information for an object (e.g. a person) as it moves betweenthe field of views of adjacent sensors. In this configuration, thetracking system tracks an object's movement within the field of view ofa first sensor and then hands off tracking information (e.g. an objectidentifier) for the object as it enters the field of view of a secondadjacent sensor. This configuration will be described in more detailusing FIGS. 10 and 11.

In one embodiment, the tracking system is configured to detect shelfinteractions using a virtual curtain. In this configuration, thetracking system is configured to process an image captured by a sensorto determine where a person is interacting with a shelf of a rack. Thetracking system uses a predetermined zone within the image as a virtualcurtain that is used to determine which region and which shelf of a rackthat a person is interacting with. This configuration will be describedin more detail using FIGS. 12-14.

In one embodiment, the tracking system is configured to detect when anitem has been picked up from a rack and to determine which person toassign the item to using a predefined zone that is associated with therack. In this configuration, the tracking system detects that an itemhas been picked up using a weight sensor. The tracking system then usesa sensor to identify a person within a predefined zone that isassociated with the rack. Once the item and the person have beenidentified, the tracking system will add the item to a digital cart thatis associated with the identified person. This configuration will bedescribed in more detail using FIGS. 15 and 18.

In one embodiment, the tracking system is configured to identify anobject that has a non-uniform weight and to assign the item to aperson's digital cart. In this configuration, the tracking system uses asensor to identify markers (e.g. text or symbols) on an item that hasbeen picked up. The tracking system uses the identified markers to thenidentify which item was picked up. The tracking system then uses thesensor to identify a person within a predefined zone that is associatedwith the rack. Once the item and the person have been identified, thetracking system will add the item to a digital cart that is associatedwith the identified person. This configuration will be described in moredetail using FIGS. 16 and 18.

In one embodiment, the tracking system is configured to detect andidentify items that have been misplaced on a rack. For example, a personmay put back an item in the wrong location on the rack. In thisconfiguration, the tracking system uses a weight sensor to detect thatan item has been put back on rack and to determine that the item is notin the correct location based on its weight. The tracking system thenuses a sensor to identify the person that put the item on the rack andanalyzes their digital cart to determine which item they put back basedon the weights of the items in their digital cart. This configurationwill be described in more detail using FIGS. 17 and 18.

In one embodiment, the tracking system is configured to determine pixelregions from images generated by each sensor which should be excludedduring object tracking. These pixel regions, or “auto-exclusion zones,”may be updated regularly (e.g., during times when there are no peoplemoving through a space). The auto-exclusion zones may be used togenerate a map of the physical portions of the space that are excludedduring tracking. This configuration is described in more detail usingFIGS. 19 through 21.

In one embodiment, the tracking system is configured to distinguishbetween closely spaced people in a space. For instance, when two peopleare standing, or otherwise located, near each other, it may be difficultor impossible for a previous systems to distinguish between thesepeople, particularly based on top-view images. In this embodiment, thesystem identifies contours at multiple depths in top-view depth imagesin order to individually detect closely spaced objects. Thisconfiguration is described in more detail using FIGS. 22 and 23.

In one embodiment, the tracking system is configured to track peopleboth locally (e.g., by tracking pixel positions in images received fromeach sensor) and globally (e.g., by tracking physical positions on aglobal plane corresponding to the physical coordinates in the space).Person tracking may be more reliable when performed both locally andglobally. For example, if a person is “lost” locally (e.g., if a sensorfails to capture a frame and a person is not detected by the sensor),the person may still be tracked globally based on an image from a nearbysensor, an estimated local position of the person determined using alocal tracking algorithm, and/or an estimated global position determinedusing a global tracking algorithm. This configuration is described inmore detail using FIGS. 24A-C through 26.

In one embodiment, the tracking system is configured to maintain arecord, which is referred to in this disclosure as a “candidate list,”of possible person identities, or identifiers (i.e., the usernames,account numbers, etc. of the people being tracked), during tracking. Acandidate list is generated and updated during tracking to establish thepossible identities of each tracked person. Generally, for each possibleidentity or identifier of a tracked person, the candidate list alsoincludes a probability that the identity, or identifier, is believed tobe correct. The candidate list is updated following interactions (e.g.,collisions) between people and in response to other uncertainty events(e.g., a loss of sensor data, imaging errors, intentional trickery,etc.). This configuration is described in more detail using FIGS. 27 and28.

In one embodiment, the tracking system is configured to employ aspecially structured approach for object re-identification when theidentity of a tracked person becomes uncertain or unknown (e.g., basedon the candidate lists described above). For example, rather thanrelying heavily on resource-expensive machine learning-based approachesto re-identify people, “lower-cost” descriptors related to observablecharacteristics (e.g., height, color, width, volume, etc.) of people areused first for person re-identification. “Higher-cost” descriptors(e.g., determined using artificial neural network models) are used whenthe lower-cost descriptors cannot provide reliable results. Forinstance, in some cases, a person may first be re-identified based onhis/her height, hair color, and/or shoe color. However, if thesedescriptors are not sufficient for reliably re-identifying the person(e.g., because other people being tracked have similar characteristics),progressively higher-level approaches may be used (e.g., involvingartificial neural networks that are trained to recognize people) whichmay be more effective at person identification but which generallyinvolve the use of more processing resources. These configurations aredescribed in more detail using FIGS. 29 through 32.

In one embodiment, the tracking system is configured to employ a cascadeof algorithms (e.g., from more simple approaches based on relativelystraightforwardly determined image features to more complex strategiesinvolving artificial neural networks) to assign an item picked up from arack to the correct person. The cascade may be triggered, for example,by (i) the proximity of two or more people to the rack, (ii) a handcrossing into the zone (or a “virtual curtain”) adjacent to the rack,and/or (iii) a weight signal indicating an item was removed from therack. In yet another embodiment, the tracking system is configured toemploy a unique contour-based approach to assign an item to the correctperson. For instance, if two people may be reaching into a rack to pickup an item, a contour may be “dilated” from a head height to a lowerheight in order to determine which person's arm reached into the rack topick up the item. If the results of this computationally efficientcontour-based approach do not satisfy certain confidence criteria, amore computationally expensive approach may be used involving poseestimation. These configurations are described in more detail usingFIGS. 33A-C through 35.

In one embodiment, the tracking system is configured to track an itemafter it exits a rack, identify a position at which the item stopsmoving, and determines which person is nearest to the stopped item. Thenearest person is generally assigned the item. This configuration may beused, for instance, when an item cannot be assigned to the correctperson even using an artificial neural network for pose estimation. Thisconfiguration is described in more detail using FIGS. 36A,B and 37.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic diagram of an embodiment of a tracking systemconfigured to track objects within a space;

FIG. 2 is a flowchart of an embodiment of a sensor mapping method forthe tracking system;

FIG. 3 is an example of a sensor mapping process for the trackingsystem;

FIG. 4 is an example of a frame from a sensor in the tracking system;

FIG. 5A is an example of a sensor mapping for a sensor in the trackingsystem;

FIG. 5B is another example of a sensor mapping for a sensor in thetracking system;

FIG. 6 is a flowchart of an embodiment of a sensor mapping method forthe tracking system using a marker grid;

FIG. 7 is an example of a sensor mapping process for the tracking systemusing a marker grid;

FIG. 8 is a flowchart of an embodiment of a shelf position calibrationmethod for the tracking system;

FIG. 9 is an example of a shelf position calibration process for thetracking system;

FIG. 10 is a flowchart of an embodiment of a tracking hand off methodfor the tracking system;

FIG. 11 is an example of a tracking hand off process for the trackingsystem;

FIG. 12 is a flowchart of an embodiment of a shelf interaction detectionmethod for the tracking system;

FIG. 13 is a front view of an example of a shelf interaction detectionprocess for the tracking system;

FIG. 14 is an overhead view of an example of a shelf interactiondetection process for the tracking system;

FIG. 15 is a flowchart of an embodiment of an item assigning method forthe tracking system;

FIG. 16 is a flowchart of an embodiment of an item identification methodfor the tracking system;

FIG. 17 is a flowchart of an embodiment of a misplaced itemidentification method for the tracking system;

FIG. 18 is an example of an item identification process for the trackingsystem;

FIG. 19 is a diagram illustrating the determination and use ofauto-exclusion zones by the tracking system;

FIG. 20 is an example auto-exclusion zone map generated by the trackingsystem;

FIG. 21 is a flowchart illustrating an example method of generating andusing auto-exclusion zones for object tracking using the trackingsystem;

FIG. 22 is a diagram illustrating the detection of closely spacedobjects using the tracking system;

FIG. 23 is a flowchart illustrating an example method of detectingclosely spaced objects using the tracking system;

FIGS. 24A-C are diagrams illustrating the tracking of a person in localimage frames and in the global plane of space 102 using the trackingsystem;

FIGS. 25A-B illustrate the implementation of a particle filter trackerby the tracking system;

FIG. 26 is a flow diagram illustrating an example method of local andglobal object tracking using the tracking system;

FIG. 27 is a diagram illustrating the use of candidate lists for objectidentification during object tracking by the tracking system;

FIG. 28 is a flowchart illustrating an example method of maintainingcandidate lists during object tracking by the tracking system;

FIG. 29 is a diagram illustrating an example tracking subsystem for usein the tracking system;

FIG. 30 is a diagram illustrating the determination of descriptors basedon object features using the tracking system;

FIGS. 31A-C are diagrams illustrating the use of descriptors forre-identification during object tracking by the tracking system;

FIG. 32 is a flowchart illustrating an example method of objectre-identification during object tracking using the tracking system;

FIGS. 33A-C are diagrams illustrating the assignment of an item to aperson using the tracking system;

FIG. 34 is a flowchart of an example method for assigning an item to aperson using the tracking system;

FIG. 35 is a flowchart of an example method of contour dilation-baseditem assignment using the tracking system;

FIGS. 36A-B are diagrams illustrating item tracking-based itemassignment using the tracking system;

FIG. 37 is a flowchart of an example method of item tracking-based itemassignment using the tracking system; and

FIG. 38 is an embodiment of a device configured to track objects withina space.

DETAILED DESCRIPTION

Position tracking systems are used to track the physical positions ofpeople and/or objects in a physical space (e.g., a store). These systemstypically use a sensor (e.g., a camera) to detect the presence of aperson and/or object and a computer to determine the physical positionof the person and/or object based on signals from the sensor. In a storesetting, other types of sensors can be installed to track the movementof inventory within the store. For example, weight sensors can beinstalled on racks and shelves to determine when items have been removedfrom those racks and shelves. By tracking both the positions of personsin a store and when items have been removed from shelves, it is possiblefor the computer to determine which person in the store removed the itemand to charge that person for the item without needing to ring up theitem at a register. In other words, the person can walk into the store,take items, and leave the store without stopping for the conventionalcheckout process.

For larger physical spaces (e.g., convenience stores and grocerystores), additional sensors can be installed throughout the space totrack the position of people and/or objects as they move about thespace. For example, additional cameras can be added to track positionsin the larger space and additional weight sensors can be added to trackadditional items and shelves. Increasing the number of cameras poses atechnical challenge because each camera only provides a field of viewfor a portion of the physical space. This means that information fromeach camera needs to be processed independently to identify and trackpeople and objects within the field of view of a particular camera. Theinformation from each camera then needs to be combined and processed asa collective in order to track people and objects within the physicalspace.

Additional information is disclosed in U.S. patent application Ser. No.______entitled, “Scalable Position Tracking System For Tracking PositionIn Large Spaces” (attorney docket no. 090278.0176) and U.S. patentapplication Ser. No. ______ entitled, “Customer-Based Video Feed”(attorney docket no. 090278.0187) which are both hereby incorporated byreference herein as if reproduced in their entirety.

Tracking System Overview

FIG. 1 is a schematic diagram of an embodiment of a tracking system 100that is configured to track objects within a space 102. As discussedabove, the tracking system 100 may be installed in a space 102 (e.g. astore) so that shoppers need not engage in the conventional checkoutprocess. Although the example of a store is used in this disclosure,this disclosure contemplates that the tracking system 100 may beinstalled and used in any type of physical space (e.g. a room, anoffice, an outdoor stand, a mall, a supermarket, a convenience store, apop-up store, a warehouse, a storage center, an amusement park, anairport, an office building, etc.). Generally, the tracking system 100(or components thereof) is used to track the positions of people and/orobjects within these spaces 102 for any suitable purpose. For example,at an airport, the tracking system 100 can track the positions oftravelers and employees for security purposes. As another example, at anamusement park, the tracking system 100 can track the positions of parkguests to gauge the popularity of attractions. As yet another example,at an office building, the tracking system 100 can track the positionsof employees and staff to monitor their productivity levels.

In FIG. 1, the space 102 is a store that comprises a plurality of itemsthat are available for purchase. The tracking system 100 may beinstalled in the store so that shoppers need not engage in theconventional checkout process to purchase items from the store. In thisexample, the store may be a convenience store or a grocery store. Inother examples, the store may not be a physical building, but a physicalspace or environment where shoppers may shop. For example, the store maybe a grab and go pantry at an airport, a kiosk in an office building, anoutdoor market at a park, etc.

In FIG. 1, the space 102 comprises one or more racks 112. Each rack 112comprises one or more shelves that are configured to hold and displayitems. In some embodiments, the space 102 may comprise refrigerators,coolers, freezers, or any other suitable type of furniture for holdingor displaying items for purchase. The space 102 may be configured asshown or in any other suitable configuration.

In this example, the space 102 is a physical structure that includes anentryway through which shoppers can enter and exit the space 102. Thespace 102 comprises an entrance area 114 and an exit area 116. In someembodiments, the entrance area 114 and the exit area 116 may overlap orare the same area within the space 102. The entrance area 114 isadjacent to an entrance (e.g. a door) of the space 102 where a personenters the space 102. In some embodiments, the entrance area 114 maycomprise a turnstile or gate that controls the flow of traffic into thespace 102. For example, the entrance area 114 may comprise a turnstilethat only allows one person to enter the space 102 at a time. Theentrance area 114 may be adjacent to one or more devices (e.g. sensors108 or a scanner 115) that identify a person as they enter space 102. Asan example, a sensor 108 may capture one or more images of a person asthey enter the space 102. As another example, a person may identifythemselves using a scanner 115. Examples of scanners 115 include, butare not limited to, a QR code scanner, a barcode scanner, a near-fieldcommunication (NFC) scanner, or any other suitable type of scanner thatcan receive an electronic code embedded with information that uniquelyidentifies a person. For instance, a shopper may scan a personal device(e.g. a smart phone) on a scanner 115 to enter the store. When theshopper scans their personal device on the scanner 115, the personaldevice may provide the scanner 115 with an electronic code that uniquelyidentifies the shopper. After the shopper is identified and/orauthenticated, the shopper is allowed to enter the store. In oneembodiment, each shopper may have a registered account with the store toreceive an identification code for the personal device.

After entering the space 102, the shopper may move around the interiorof the store. As the shopper moves throughout the space 102, the shoppermay shop for items by removing items from the racks 112. The shopper canremove multiple items from the racks 112 in the store to purchase thoseitems. When the shopper has finished shopping, the shopper may leave thestore via the exit area 116. The exit area 116 is adjacent to an exit(e.g. a door) of the space 102 where a person leaves the space 102. Insome embodiments, the exit area 116 may comprise a turnstile or gatethat controls the flow of traffic out of the space 102. For example, theexit area 116 may comprise a turnstile that only allows one person toleave the space 102 at a time. In some embodiments, the exit area 116may be adjacent to one or more devices (e.g. sensors 108 or a scanner115) that identify a person as they leave the space 102. For example, ashopper may scan their personal device on the scanner 115 before aturnstile or gate will open to allow the shopper to exit the store. Whenthe shopper scans their personal device on the scanner 115, the personaldevice may provide an electronic code that uniquely identifies theshopper to indicate that the shopper is leaving the store. When theshopper leaves the store, an account for the shopper is charged for theitems that the shopper removed from the store. Through this process thetracking system 100 allows the shopper to leave the store with theiritems without engaging in a conventional checkout process.

Global Plane Overview

In order to describe the physical location of people and objects withinthe space 102, a global plane 104 is defined for the space 102. Theglobal plane 104 is a user-defined coordinate system that is used by thetracking system 100 to identify the locations of objects within aphysical domain (i.e. the space 102). Referring to FIG. 1 as an example,a global plane 104 is defined such that an x-axis and a y-axis areparallel with a floor of the space 102. In this example, the z-axis ofthe global plane 104 is perpendicular to the floor of the space 102. Alocation in the space 102 is defined as a reference location 101 ororigin for the global plane 104. In FIG. 1, the global plane 104 isdefined such that reference location 101 corresponds with a corner ofthe store. In other examples, the reference location 101 may be locatedat any other suitable location within the space 102.

In this configuration, physical locations within the space 102 can bedescribed using (x,y) coordinates in the global plane 104. As anexample, the global plane 104 may be defined such that one unit in theglobal plane 104 corresponds with one meter in the space 102. In otherwords, an x-value of one in the global plane 104 corresponds with anoffset of one meter from the reference location 101 in the space 102. Inthis example, a person that is standing in the corner of the space 102at the reference location 101 will have an (x,y) coordinate with a valueof (0,0) in the global plane 104. If person moves two meters in thepositive x-axis direction and two meters in the positive y-axisdirection, then their new (x,y) coordinate will have a value of (2,2).In other examples, the global plane 104 may be expressed using inches,feet, or any other suitable measurement units.

Once the global plane 104 is defined for the space 102, the trackingsystem 100 uses (x,y) coordinates of the global plane 104 to track thelocation of people and objects within the space 102. For example, as ashopper moves within the interior of the store, the tracking system 100may track their current physical location within the store using (x,y)coordinates of the global plane 104.

Tracking System Hardware

In one embodiment, the tracking system 100 comprises one or more clients105, one or more servers 106, one or more scanners 115, one or moresensors 108, and one or more weight sensors 110. The one or more clients105, one or more servers 106, one or more scanners 115, one or moresensors 108, and one or more weight sensors 110 may be in signalcommunication with each other over a network 107. The network 107 may beany suitable type of wireless and/or wired network including, but notlimited to, all or a portion of the Internet, an Intranet, a Bluetoothnetwork, a WIFI network, a Zigbee network, a Z-wave network, a privatenetwork, a public network, a peer-to-peer network, the public switchedtelephone network, a cellular network, a local area network (LAN), ametropolitan area network (MAN), a wide area network (WAN), and asatellite network. The network 107 may be configured to support anysuitable type of communication protocol as would be appreciated by oneof ordinary skill in the art. The tracking system 100 may be configuredas shown or in any other suitable configuration.

Sensors

The tracking system 100 is configured to use sensors 108 to identify andtrack the location of people and objects within the space 102. Forexample, the tracking system 100 uses sensors 108 to capture images orvideos of a shopper as they move within the store. The tracking system100 may process the images or videos provided by the sensors 108 toidentify the shopper, the location of the shopper, and/or any items thatthe shopper picks up.

Examples of sensors 108 include, but are not limited to, cameras, videocameras, web cameras, printed circuit board (PCB) cameras, depth sensingcameras, time-of-flight cameras, LiDARs, structured light cameras, orany other suitable type of imaging device.

Each sensor 108 is positioned above at least a portion of the space 102and is configured to capture overhead view images or videos of at leasta portion of the space 102. In one embodiment, the sensors 108 aregenerally configured to produce videos of portions of the interior ofthe space 102. These videos may include frames or images 302 of shopperswithin the space 102. Each frame 302 is a snapshot of the people and/orobjects within the field of view of a particular sensor 108 at aparticular moment in time. A frame 302 may be a two-dimensional (2D)image or a three-dimensional (3D) image (e.g. a point cloud or a depthmap). In this configuration, each frame 302 is of a portion of a globalplane 104 for the space 102. Referring to FIG. 4 as an example, a frame302 comprises a plurality of pixels that are each associated with apixel location 402 within the frame 302. The tracking system 100 usespixel locations 402 to describe the location of an object with respectto pixels in a frame 302 from a sensor 108. In the example shown in FIG.4, the tracking system 100 can identify the location of different marker304 within the frame 302 using their respective pixel locations 402. Thepixel location 402 corresponds with a pixel row and a pixel column wherea pixel is located within the frame 302. In one embodiment, each pixelis also associated with a pixel value 404 that indicates a depth ordistance measurement in the global plane 104. For example, a pixel value404 may correspond with a distance between a sensor 108 and a surface inthe space 102.

Each sensor 108 has a limited field of view within the space 102. Thismeans that each sensor 108 may only be able to capture a portion of thespace 102 within their field of view. To provide complete coverage ofthe space 102, the tracking system 100 may use multiple sensors 108configured as a sensor array. In FIG. 1, the sensors 108 are configuredas a three by four sensor array. In other examples, a sensor array maycomprise any other suitable number and/or configuration of sensors 108.In one embodiment, the sensor array is positioned parallel with thefloor of the space 102. In some embodiments, the sensor array isconfigured such that adjacent sensors 108 have at least partiallyoverlapping fields of view. In this configuration, each sensor 108captures images or frames 302 of a different portion of the space 102which allows the tracking system 100 to monitor the entire space 102 bycombining information from frames 302 of multiple sensors 108. Thetracking system 100 is configured to map pixel locations 402 within eachsensor 108 to physical locations in the space 102 using homographies118. A homography 118 is configured to translate between pixel locations402 in a frame 302 captured by a sensor 108 and (x,y) coordinates in theglobal plane 104 (i.e. physical locations in the space 102). Thetracking system 100 uses homographies 118 to correlate between a pixellocation 402 in a particular sensor 108 with a physical location in thespace 102. In other words, the tracking system 100 uses homographies 118to determine where a person is physically located in the space 102 basedon their pixel location 402 within a frame 302 from a sensor 108. Sincethe tracking system 100 uses multiple sensors 108 to monitor the entirespace 102, each sensor 108 is uniquely associated with a differenthomography 118 based on the sensor's 108 physical location within thespace 102. This configuration allows the tracking system 100 todetermine where a person is physically located within the entire space102 based on which sensor 108 they appear in and their location within aframe 302 captured by that sensor 108. Additional information abouthomographies 118 is described in FIGS. 2-7.

Weight Sensors

The tracking system 100 is configured to use weight sensors 110 todetect and identify items that a person picks up within the space 102.For example, the tracking system 100 uses weight sensors 110 that arelocated on the shelves of a rack 112 to detect when a shopper removes anitem from the rack 112. Each weight sensor 110 may be associated with aparticular item which allows the tracking system 100 to identify whichitem the shopper picked up.

A weight sensor 110 is generally configured to measure the weight ofobjects (e.g. products) that are placed on or near the weight sensor110. For example, a weight sensor 110 may comprise a transducer thatconverts an input mechanical force (e.g. weight, tension, compression,pressure, or torque) into an output electrical signal (e.g. current orvoltage). As the input force increases, the output electrical signal mayincrease proportionally. The tracking system 100 is configured toanalyze the output electrical signal to determine an overall weight forthe items on the weight sensor 110.

Examples of weight sensors 110 include, but are not limited to, apiezoelectric load cell or a pressure sensor. For example, a weightsensor 110 may comprise one or more load cells that are configured tocommunicate electrical signals that indicate a weight experienced by theload cells. For instance, the load cells may produce an electricalcurrent that varies depending on the weight or force experienced by theload cells. The load cells are configured to communicate the producedelectrical signals to a server 105 and/or a client 106 for processing.

Weight sensors 110 may be positioned onto furniture (e.g. racks 112)within the space 102 to hold one or more items. For example, one or moreweight sensors 110 may be positioned on a shelf of a rack 112. Asanother example, one or more weight sensors 110 may be positioned on ashelf of a refrigerator or a cooler. As another example, one or moreweight sensors 110 may be integrated with a shelf of a rack 112. Inother examples, weight sensors 110 may be positioned in any othersuitable location within the space 102.

In one embodiment, a weight sensor 110 may be associated with aparticular item. For instance, a weight sensor 110 may be configured tohold one or more of a particular item and to measure a combined weightfor the items on the weight sensor 110. When an item is picked up fromthe weight sensor 110, the weight sensor 110 is configured to detect aweight decrease. In this example, the weight sensor 110 is configured touse stored information about the weight of the item to determine anumber of items that were removed from the weight sensor 110. Forexample, a weight sensor 110 may be associated with an item that has anindividual weight of eight ounces. When the weight sensor 110 detects aweight decrease of twenty-four ounces, the weight sensor 110 maydetermine that three of the items were removed from the weight sensor110. The weight sensor 110 is also configured to detect a weightincrease when an item is added to the weight sensor 110. For example, ifan item is returned to the weight sensor 110, then the weight sensor 110will determine a weight increase that corresponds with the individualweight for the item associated with the weight sensor 110.

Servers

A server 106 may be formed by one or more physical devices configured toprovide services and resources (e.g. data and/or hardware resources) forthe tracking system 100. Additional information about the hardwareconfiguration of a server 106 is described in FIG. 38. In oneembodiment, a server 106 may be operably coupled to one or more sensors108 and/or weight sensors 110. The tracking system 100 may comprise anysuitable number of servers 106. For example, the tracking system 100 maycomprise a first server 106 that is in signal communication with a firstplurality of sensors 108 in a sensor array and a second server 106 thatis in signal communication with a second plurality of sensors 108 in thesensor array. As another example, the tracking system 100 may comprise afirst server 106 that is in signal communication with a plurality ofsensors 108 and a second server 106 that is in signal communication witha plurality of weight sensors 110. In other examples, the trackingsystem 100 may comprise any other suitable number of servers 106 thatare each in signal communication with one or more sensors 108 and/orweight sensors 110.

A server 106 may be configured to process data (e.g. frames 302 and/orvideo) for one or more sensors 108 and/or weight sensors 110. In oneembodiment, a server 106 may be configured to generate homographies 118for sensors 108. As discussed above, the generated homographies 118allow the tracking system 100 to determine where a person is physicallylocated within the entire space 102 based on which sensor 108 theyappear in and their location within a frame 302 captured by that sensor108. In this configuration, the server 106 determines coefficients for ahomography 118 based on the physical location of markers in the globalplane 104 and the pixel locations of the markers in an image from asensor 108. Examples of the server 106 performing this process aredescribed in FIGS. 2-7.

In one embodiment, a server 106 is configured to calibrate a shelfposition within the global plane 104 using sensors 108. This processallows the tracking system 100 to detect when a rack 112 or sensor 108has moved from its original location within the space 102. In thisconfiguration, the server 106 periodically compares the current shelflocation of a rack 112 to an expected shelf location for the rack 112using a sensor 108. In the event that the current shelf location doesnot match the expected shelf location, then the server 106 will use oneor more other sensors 108 to determine whether the rack 112 has moved orwhether the first sensor 108 has moved. An example of the server 106performing this process is described in FIGS. 8 and 9.

In one embodiment, a server 106 is configured to hand off trackinginformation for an object (e.g. a person) as it moves between the fieldsof view of adjacent sensors 108. This process allows the tracking system100 to track people as they move within the interior of the space 102.In this configuration, the server 106 tracks an object's movement withinthe field of view of a first sensor 108 and then hands off trackinginformation (e.g. an object identifier) for the object as it enters thefield of view of a second adjacent sensor 108. An example of the server106 performing this process is described in FIGS. 10 and 11.

In one embodiment, a server 106 is configured to detect shelfinteractions using a virtual curtain. This process allows the trackingsystem 100 to identify items that a person picks up from a rack 112. Inthis configuration, the server 106 is configured to process an imagecaptured by a sensor 108 to determine where a person is interacting witha shelf of a rack 112. The server 106 uses a predetermined zone withinthe image as a virtual curtain that is used to determine which regionand which shelf of a rack 112 that a person is interacting with. Anexample of the server 106 performing this process is described in FIGS.12-14.

In one embodiment, a server 106 is configured to detect when an item hasbeen picked up from a rack 112 and to determine which person to assignthe item to using a predefined zone that is associated with the rack112. This process allows the tracking system 100 to associate items on arack 112 with the person that picked up the item. In this configuration,the server 106 detects that an item has been picked up using a weightsensor 110. The server 106 then uses a sensor 108 to identify a personwithin a predefined zone that is associated with the rack 112. Once theitem and the person have been identified, the server 106 will add theitem to a digital cart that is associated with the identified person. Anexample of the server 106 performing this process is described in FIGS.15 and 18.

In one embodiment, a server 106 is configured to identify an object thathas a non-uniform weight and to assign the item to a person's digitalcart. This process allows the tracking system 100 to identify items thata person picks up that cannot be identified based on just their weight.For example, the weight of fresh food is not constant and will vary fromitem to item. In this configuration, the server 106 uses a sensor 108 toidentify markers (e.g. text or symbols) on an item that has been pickedup. The server 106 uses the identified markers to then identify whichitem was picked up. The server 106 then uses the sensor 108 to identifya person within a predefined zone that is associated with the rack 112.Once the item and the person have been identified, the server 106 willadd the item to a digital cart that is associated with the identifiedperson. An example of the server 106 performing this process isdescribed in FIGS. 16 and 18.

In one embodiment, a server 106 is configured to identify items thathave been misplaced on a rack 112. This process allows the trackingsystem 100 to remove items from a shopper's digital cart when theshopper puts down an item regardless of whether they put the item backin its proper location. For example, a person may put back an item inthe wrong location on the rack 112 or on the wrong rack 112. In thisconfiguration, the server 106 uses a weight sensor 110 to detect that anitem has been put back on rack 112 and to determine that the item is notin the correct location based on its weight. The server 106 then uses asensor 108 to identify the person that put the item on the rack 112 andanalyzes their digital cart to determine which item they put back basedon the weights of the items in their digital cart. An example of theserver 106 performing this process is described in FIGS. 17 and 18.

Clients

In some embodiments, one or more sensors 108 and/or weight sensors 110are operably coupled to a server 106 via a client 105. In oneembodiment, the tracking system 100 comprises a plurality of clients 105that may each be operably coupled to one or more sensors 108 and/orweight sensors 110. For example, first client 105 may be operablycoupled to one or more sensors 108 and/or weight sensors 110 and asecond client 105 may be operably coupled to one or more other sensors108 and/or weight sensors 110. A client 105 may be formed by one or morephysical devices configured to process data (e.g. frames 302 and/orvideo) for one or more sensors 108 and/or weight sensors 110. A client105 may act as an intermediary for exchanging data between a server 106and one or more sensors 108 and/or weight sensors 110. The combinationof one or more clients 105 and a server 106 may also be referred to as atracking subsystem. In this configuration, a client 105 may beconfigured to provide image processing capabilities for images or frames302 that are captured by a sensor 108. The client 105 is furtherconfigured to send images, processed images, or any other suitable typeof data to the server 106 for further processing and analysis. In someembodiments, a client 105 may be configured to perform one or more ofthe processes described above for the server 106.

Sensor Mapping Process

FIG. 2 is a flowchart of an embodiment of a sensor mapping method 200for the tracking system 100. The tracking system 100 may employ method200 to generate a homography 118 for a sensor 108. As discussed above, ahomography 118 allows the tracking system 100 to determine where aperson is physically located within the entire space 102 based on whichsensor 108 they appear in and their location within a frame 302 capturedby that sensor 108. Once generated, the homography 118 can be used totranslate between pixel locations 402 in images (e.g. frames 302)captured by a sensor 108 and (x,y) coordinates 306 in the global plane104 (i.e. physical locations in the space 102). The following is anon-limiting example of the process for generating a homography 118 forsingle sensor 108. This same process can be repeated for generating ahomography 118 for other sensors 108.

At step 202, the tracking system 100 receives (x,y) coordinates 306 formarkers 304 in the space 102. Referring to FIG. 3 as an example, eachmarker 304 is an object that identifies a known physical location withinthe space 102. The markers 304 are used to demarcate locations in thephysical domain (i.e. the global plane 104) that can be mapped to pixellocations 402 in a frame 302 from a sensor 108. In this example, themarkers 304 are represented as stars on the floor of the space 102. Amarker 304 may be formed of any suitable object that can be observed bya sensor 108. For example, a marker 304 may be tape or a sticker that isplaced on the floor of the space 102. As another example, a marker 304may be a design or marking on the floor of the space 102. In otherexamples, markers 304 may be positioned in any other suitable locationwithin the space 102 that is observable by a sensor 108. For instance,one or more markers 304 may be positioned on top of a rack 112.

In one embodiment, the (x,y) coordinates 306 for markers 304 areprovided by an operator. For example, an operator may manually placemarkers 304 on the floor of the space 102. The operator may determine an(x,y) location 306 for a marker 304 by measuring the distance betweenthe marker 304 and the reference location 101 for the global plane 104.The operator may then provide the determined (x,y) location 306 to aserver 106 or a client 105 of the tracking system 100 as an input.

Referring to the example in FIG. 3, the tracking system 100 may receivea first (x,y) coordinate 306A for a first marker 304A in a space 102 anda second (x,y) coordinate 306B for a second marker 304B in the space102. The first (x,y) coordinate 306A describes the physical location ofthe first marker 304A with respect to the global plane 104 of the space102. The second (x,y) coordinate 306B describes the physical location ofthe second marker 304B with respect to the global plane 104 of the space102. The tracking system 100 may repeat the process of obtaining (x,y)coordinates 306 for any suitable number of additional markers 304 withinthe space 102.

Once the tracking system 100 knows the physical location of the markers304 within the space 102, the tracking system 100 then determines wherethe markers 304 are located with respect to the pixels in the frame 302of a sensor 108. Returning to FIG. 2 at step 204, the tracking system100 receives a frame 302 from a sensor 108. Referring to FIG. 4 as anexample, the sensor 108 captures an image or frame 302 of the globalplane 104 for at least a portion of the space 102. In this example, theframe 302 comprises a plurality of markers 304.

Returning to FIG. 2 at step 206, the tracking system 100 identifiesmarkers 304 within the frame 302 of the sensor 108. In one embodiment,the tracking system 100 uses object detection to identify markers 304within the frame 302. For example, the markers 304 may have knownfeatures (e.g. shape, pattern, color, text, etc.) that the trackingsystem 100 can search for within the frame 302 to identify a marker 304.Referring to the example in FIG. 3, each marker 304 has a star shape. Inthis example, the tracking system 100 may search the frame 302 for starshaped objects to identify the markers 304 within the frame 302. Thetracking system 100 may identify the first marker 304A, the secondmarker 304B, and any other markers 304 within the frame 302. In otherexamples, the tracking system 100 may use any other suitable featuresfor identifying markers 304 within the frame 302. In other embodiments,the tracking system 100 may employ any other suitable image processingtechnique for identifying markers 302 with the frame 302. For example,the markers 304 may have a known color or pixel value. In this example,the tracking system 100 may use thresholds to identify the markers 304within frame 302 that correspond with the color or pixel value of themarkers 304.

Returning to FIG. 2 at step 208, the tracking system 100 determines thenumber of identified markers 304 within the frame 302. Here, trackingsystem 100 counts the number of markers 304 that were detected withinthe frame 302. Referring to the example in FIG. 3, the tracking system100 detects eight markers 304 within the frame 302.

Returning to FIG. 2 at step 210, the tracking system 100 determineswhether the number of identified markers 304 is greater than or equal toa predetermined threshold value. In some embodiments, the predeterminedthreshold value is proportional to a level of accuracy for generating ahomography 118 for a sensor 108. Increasing the predetermined thresholdvalue may increase the accuracy when generating a homography 118 whiledecreasing the predetermined threshold value may decrease the accuracywhen generating a homography 118. As an example, the predeterminedthreshold value may be set to a value of six. In the example shown inFIG. 3, the tracking system 100 identified eight markers 304 which isgreater than the predetermined threshold value. In other examples, thepredetermined threshold value may be set to any other suitable value.The tracking system 100 returns to step 204 in response to determiningthat the number of identified markers 304 is less than the predeterminedthreshold value. In this case, the tracking system 100 returns to step204 to capture another frame 302 of the space 102 using the same sensor108 to try to detect more markers 304. Here, the tracking system 100tries to obtain a new frame 302 that includes a number of markers 304that is greater than or equal to the predetermined threshold value. Forexample, the tracking system 100 may receive new frame 302 of the space102 after an operator adds one or more additional markers 304 to thespace 102. As another example, the tracking system 100 may receive newframe 302 after lighting conditions have been changed to improve thedetectability of the markers 304 within the frame 302. In otherexamples, the tracking system 100 may receive new frame 302 after anykind of change that improves the detectability of the markers 304 withinthe frame 302.

The tracking system 100 proceeds to step 212 in response to determiningthat the number of identified markers 304 is greater than or equal tothe predetermined threshold value. At step 212, the tracking system 100determines pixel locations 402 in the frame 302 for the identifiedmarkers 304. For example, the tracking system 100 determines a firstpixel location 402A within the frame 302 that corresponds with the firstmarker 304A and a second pixel location 402B within the frame 302 thatcorresponds with the second marker 304B. The first pixel location 402Acomprises a first pixel row and a first pixel column indicating wherethe first marker 304A is located in the frame 302. The second pixellocation 402B comprises a second pixel row and a second pixel columnindicating where the second marker 304B is located in the frame 302.

At step 214, the tracking system 100 generates a homography 118 for thesensor 108 based on the pixel locations 402 of identified markers 304with the frame 302 of the sensor 108 and the (x,y) coordinate 306 of theidentified markers 304 in the global plane 104. In one embodiment, thetracking system 100 correlates the pixel location 402 for each of theidentified markers 304 with its corresponding (x,y) coordinate 306.Continuing with the example in FIG. 3, the tracking system 100associates the first pixel location 402A for the first marker 304A withthe first (x,y) coordinate 306A for the first marker 304A. The trackingsystem 100 also associates the second pixel location 402B for the secondmarker 304B with the second (x,y) coordinate 306B for the second marker304B. The tracking system 100 may repeat the process of associatingpixel locations 402 and (x,y) coordinates 306 for all of the identifiedmarkers 304.

The tracking system 100 then determines a relationship between the pixellocations 402 of identified markers 304 with the frame 302 of the sensor108 and the (x,y) coordinates 306 of the identified markers 304 in theglobal plane 104 to generate a homography 118 for the sensor 108. Thegenerated homography 118 allows the tracking system 100 to map pixellocations 402 in a frame 302 from the sensor 108 to (x,y) coordinates306 in the global plane 104. Additional information about a homography118 is described in FIGS. 5A and 5B. Once the tracking system 100generates the homography 118 for the sensor 108, the tracking system 100stores an association between the sensor 108 and the generatedhomography 118 in memory (e.g. memory 3804).

The tracking system 100 may repeat the process described above togenerate and associate homographies 118 with other sensors 108.Continuing with the example in FIG. 3, the tracking system 100 mayreceive a second frame 302 from a second sensor 108. In this example,the second frame 302 comprises the first marker 304A and the secondmarker 304B. The tracking system 100 may determine a third pixellocation 402 in the second frame 302 for the first marker 304A, a fourthpixel location 402 in the second frame 302 for the second marker 304B,and pixel locations 402 for any other markers 304. The tracking system100 may then generate a second homography 118 based on the third pixellocation 402 in the second frame 302 for the first marker 304A, thefourth pixel location 402 in the second frame 302 for the second marker304B, the first (x,y) coordinate 306A in the global plane 104 for thefirst marker 304A, the second (x,y) coordinate 306B in the global plane104 for the second marker 304B, and pixel locations 402 and (x,y)coordinates 306 for other markers 304. The second homography 118comprises coefficients that translate between pixel locations 402 in thesecond frame 302 and physical locations (e.g. (x,y) coordinates 306) inthe global plane 104. The coefficients of the second homography 118 aredifferent from the coefficients of the homography 118 that is associatedwith the first sensor 108. This process uniquely associates each sensor108 with a corresponding homography 118 that maps pixel locations 402from the sensor 108 to (x,y) coordinates 306 in the global plane 104.

Homographies

An example of a homography 118 for a sensor 108 is described in FIGS. 5Aand 5B. Referring to FIG. 5A, a homography 118 comprises a plurality ofcoefficients configured to translate between pixel locations 402 in aframe 302 and physical locations (e.g. (x,y) coordinates 306) in theglobal plane 104. In this example, the homography 118 is configured as amatrix and the coefficients of the homography 118 are represented asH₁₁, H₁₂, H₁₃, H₁₄, H₂₁, H₂₂, H₂₃, H₂₄, H₃₁, H₃₂, H₃₃, H₃₄, H₄₁, H₄₂,H₄₃, and H₄₄. The tracking system 100 may generate the homography 118 bydefining a relationship or function between pixel locations 402 in aframe 302 and physical locations (e.g. (x,y) coordinates 306) in theglobal plane 104 using the coefficients. For example, the trackingsystem 100 may define one or more functions using the coefficients andmay perform a regression (e.g. least squares regression) to solve forvalues for the coefficients that project pixel locations 402 of a frame302 of a sensor to (x,y) coordinates 306 in the global plane 104.Referring to the example in FIG. 3, the homography 118 for the sensor108 is configured to project the first pixel location 402A in the frame302 for the first marker 304A to the first (x,y) coordinate 306A in theglobal plane 104 for the first marker 304A and to project the secondpixel location 402B in the frame 302 for the second marker 304B to thesecond (x,y) coordinate 306B in the global plane 104 for the secondmarker 304B. In other examples, the tracking system 100 may solve forcoefficients of the homography 118 using any other suitable technique.In the example shown in FIG. 5A, the z-value at the pixel location 402may correspond with a pixel value 404. In this case, the homography 118is further configured to translate between pixel values 404 in a frame302 and z-coordinates (e.g. heights or elevations) in the global plane104.

Using Homographies

Once the tracking system 100 generates a homography 118, the trackingsystem 100 may use the homography 118 to determine the location of anobject (e.g. a person) within the space 102 based on the pixel location402 of the object in a frame 302 of a sensor 108. For example, thetracking system 100 may perform matrix multiplication between a pixellocation 402 in a first frame 302 and a homography 118 to determine acorresponding (x,y) coordinate 306 in the global plane 104. For example,the tracking system 100 receives a first frame 302 from a sensor 108 anddetermines a first pixel location in the frame 302 for an object in thespace 102. The tracking system 100 may then apply the homography 118that is associated with the sensor 108 to the first pixel location 402of the object to determine a first (x,y) coordinate 306 that identifiesa first x-value and a first y-value in the global plane 104 where theobject is located.

In some instances, the tracking system 100 may use multiple sensors 108to determine the location of the object. Using multiple sensors 108 mayprovide more accuracy when determining where an object is located withinthe space 102. In this case, the tracking system 100 uses homographies118 that are associated with different sensors 108 to determine thelocation of an object within the global plane 104. Continuing with theprevious example, the tracking system 100 may receive a second frame 302from a second sensor 108. The tracking system 100 may determine a secondpixel location 402 in the second frame 302 for the object in the space102. The tracking system 100 may then apply a second homography 118 thatis associated the second sensor 108 to the second pixel location 402 ofthe object to determine a second (x,y) coordinate 306 that identifies asecond x-value and a second y-value in the global plane 104 where theobject is located.

When the first (x,y) coordinate 306 and the second (x,y) coordinate 306are the same, the tracking system 100 may use either the first (x,y)coordinate 306 or the second (x,y) coordinate 306 as the physicallocation of the object within the space 102. The tracking system 100 mayemploy any suitable clustering technique between the first (x,y)coordinate 306 and the second (x,y) coordinate 306 when the first (x,y)coordinate 306 and the second (x,y) coordinate 306 are not the same. Inthis case, the first (x,y) coordinate 306 and the second (x,y)coordinate 306 are different so the tracking system 100 will need todetermine the physical location of the object within the space 102 basedoff the first (x,y) location 306 and the second (x,y) location 306. Forexample, the tracking system 100 may generate an average (x,y)coordinate for the object by computing an average between the first(x,y) coordinate 306 and the second (x,y) coordinate 306. As anotherexample, the tracking system 100 may generate a median (x,y) coordinatefor the object by computing a median between the first (x,y) coordinate306 and the second (x,y) coordinate 306. In other examples, the trackingsystem 100 may employ any other suitable technique to resolvedifferences between the first (x,y) coordinate 306 and the second (x,y)coordinate 306.

The tracking system 100 may use the inverse of the homography 118 toproject from (x,y) coordinates 306 in the global plane 104 to pixellocations 402 in a frame 302 of a sensor 108. For example, the trackingsystem 100 receives an (x,y) coordinate 306 in the global plane 104 foran object. The tracking system 100 identifies a homography 118 that isassociated with a sensor 108 where the object is seen. The trackingsystem 100 may then apply the inverse homography 118 to the (x,y)coordinate 306 to determine a pixel location 402 where the object islocated in the frame 302 for the sensor 108. The tracking system 100 maycompute the matrix inverse of the homograph 500 when the homography 118is represented as a matrix. Referring to FIG. 5B as an example, thetracking system 100 may perform matrix multiplication between a (x,y)coordinates 306 in the global plane 104 and the inverse homography 118to determine a corresponding pixel location 402 in the frame 302 for thesensor 108.

Sensor Mapping Using a Marker Grid

FIG. 6 is a flowchart of an embodiment of a sensor mapping method 600for the tracking system 100 using a marker grid 702. The tracking system100 may employ method 600 to reduce the amount of time it takes togenerate a homography 118 for a sensor 108. For example, using a markergrid 702 reduces the amount of setup time required to generate ahomography 118 for a sensor 108. Typically, each marker 304 is placedwithin a space 102 and the physical location of each marker 304 isdetermined independently. This process is repeated for each sensor 108in a sensor array. In contrast, a marker grid 702 is a portable surfacethat comprises a plurality of markers 304. The marker grid 702 may beformed using carpet, fabric, poster board, foam board, vinyl, paper,wood, or any other suitable type of material. Each marker 304 is anobject that identifies a particular location on the marker grid 702.Examples of markers 304 include, but are not limited to, shapes,symbols, and text. The physical locations of each marker 304 on themarker grid 702 are known and are stored in memory (e.g. marker gridinformation 716). Using a marker grid 702 simplifies and speeds the upthe process of placing and determining the location of markers 304because the marker grid 702 and its markers 304 can be quicklyrepositioned anywhere within the space 102 without having toindividually move markers 304 or add new markers 304 to the space 102.Once generated, the homography 118 can be used to translate betweenpixel locations 402 in frame 302 captured by a sensor 108 and (x,y)coordinates 306 in the global plane 104 (i.e. physical locations in thespace 102).

At step 602, the tracking system 100 receives a first (x,y) coordinate306A for a first corner 704 of a marker grid 702 in a space 102.Referring to FIG. 7 as an example, the marker grid 702 is configured tobe positioned on a surface (e.g. the floor) within the space 102 that isobservable by one or more sensors 108. In this example, the trackingsystem 100 receives a first (x,y) coordinate 306A in the global plane104 for a first corner 704 of the marker grid 702. The first (x,y)coordinate 306A describes the physical location of the first corner 704with respect to the global plane 104. In one embodiment, the first (x,y)coordinate 306A is based on a physical measurement of a distance betweena reference location 101 in the space 102 and the first corner 704. Forexample, the first (x,y) coordinate 306A for the first corner 704 of themarker grid 702 may be provided by an operator. In this example, anoperator may manually place the marker grid 702 on the floor of thespace 102. The operator may determine an (x,y) location 306 for thefirst corner 704 of the marker grid 702 by measuring the distancebetween the first corner 704 of the marker grid 702 and the referencelocation 101 for the global plane 104. The operator may then provide thedetermined (x,y) location 306 to a server 106 or a client 105 of thetracking system 100 as an input.

In another embodiment, the tracking system 100 may receive a signal froma beacon located at the first corner 704 of the marker grid 702 thatidentifies the first (x,y) coordinate 306A. An example of a beaconincludes, but is not limited to, a Bluetooth beacon. For example, thetracking system 100 may communicate with the beacon and determine thefirst (x,y) coordinate 306A based on the time-of-flight of a signal thatis communicated between the tracking system 100 and the beacon. In otherembodiments, the tracking system 100 may obtain the first (x,y)coordinate 306A for the first corner 704 using any other suitabletechnique.

Returning to FIG. 6 at step 604, the tracking system 100 determines(x,y) coordinates 306 for the markers 304 on the marker grid 702.Returning to the example in FIG. 7, the tracking system 100 determines asecond (x,y) coordinate 306B for a first marker 304A on the marker grid702. The tracking system 100 comprises marker grid information 716 thatidentifies offsets between markers 304 on the marker grid 702 and thefirst corner 704 of the marker grid 702. In this example, the offsetcomprises a distance between the first corner 704 of the marker grid 702and the first marker 304A with respect to the x-axis and the y-axis ofthe global plane 104. Using the marker grid information 1912, thetracking system 100 is able to determine the second (x,y) coordinate306B for the first marker 304A by adding an offset associated with thefirst marker 304A to the first (x,y) coordinate 306A for the firstcorner 704 of the marker grid 702.

In one embodiment, the tracking system 100 determines the second (x,y)coordinate 306B based at least in part on a rotation of the marker grid702. For example, the tracking system 100 may receive a fourth (x,y)coordinate 306D that identifies x-value and a y-value in the globalplane 104 for a second corner 706 of the marker grid 702. The trackingsystem 100 may obtain the fourth (x,y) coordinate 306D for the secondcorner 706 of the marker grid 702 using a process similar to the processdescribed in step 602. The tracking system 100 determines a rotationangle 712 between the first (x,y) coordinate 306A for the first corner704 of the marker grid 702 and the fourth (x,y) coordinate 306D for thesecond corner 706 of the marker grid 702. In this example, the rotationangle 712 is about the first corner 704 of the marker grid 702 withinthe global plane 104. The tracking system 100 then determines the second(x,y) coordinate 306B for the first marker 304A by applying atranslation by adding the offset associated with the first marker 304Ato the first (x,y) coordinate 306A for the first corner 704 of themarker grid 702 and applying a rotation using the rotation angle 712about the first (x,y) coordinate 306A for the first corner 704 of themarker grid 702. In other examples, the tracking system 100 maydetermine the second (x,y) coordinate 306B for the first marker 304Ausing any other suitable technique.

The tracking system 100 may repeat this process for one or moreadditional markers 304 on the marker grid 702. For example, the trackingsystem 100 determines a third (x,y) coordinate 306C for a second marker304B on the marker grid 702. Here, the tracking system 100 uses themarker grid information 716 to identify an offset associated with thesecond marker 304A. The tracking system 100 is able to determine thethird (x,y) coordinate 306C for the second marker 304B by adding theoffset associated with the second marker 304B to the first (x,y)coordinate 306A for the first corner 704 of the marker grid 702. Inanother embodiment, the tracking system 100 determines a third (x,y)coordinate 306C for a second marker 304B based at least in part on arotation of the marker grid 702 using a process similar to the processdescribed above for the first marker 304A.

Once the tracking system 100 knows the physical location of the markers304 within the space 102, the tracking system 100 then determines wherethe markers 304 are located with respect to the pixels in the frame 302of a sensor 108. At step 606, the tracking system 100 receives a frame302 from a sensor 108. The frame 302 is of the global plane 104 thatincludes at least a portion of the marker grid 702 in the space 102. Theframe 302 comprises one or more markers 304 of the marker grid 702. Theframe 302 is configured similar to the frame 302 described in FIGS. 2-4.For example, the frame 302 comprises a plurality of pixels that are eachassociated with a pixel location 402 within the frame 302. The pixellocation 402 identifies a pixel row and a pixel column where a pixel islocated. In one embodiment, each pixel is associated with a pixel value404 that indicates a depth or distance measurement. For example, a pixelvalue 404 may correspond with a distance between the sensor 108 and asurface within the space 102.

At step 610, the tracking system 100 identifies markers 304 within theframe 302 of the sensor 108. The tracking system 100 may identifymarkers 304 within the frame 302 using a process similar to the processdescribed in step 206 of FIG. 2. For example, the tracking system 100may use object detection to identify markers 304 within the frame 302.Referring to the example in FIG. 7, each marker 304 is a unique shape orsymbol. In other examples, each marker 304 may have any other uniquefeatures (e.g. shape, pattern, color, text, etc.). In this example, thetracking system 100 may search for objects within the frame 302 thatcorrespond with the known features of a marker 304. Tracking system 100may identify the first marker 304A, the second marker 304B, and anyother markers 304 on the marker grid 702.

In one embodiment, the tracking system 100 compares the features of theidentified markers 304 to the features of known markers 304 on themarker grid 702 using a marker dictionary 718. The marker dictionary 718identifies a plurality of markers 304 that are associated with a markergrid 702. In this example, the tracking system 100 may identify thefirst marker 304A by identifying a star on the marker grid 702,comparing the star to the symbols in the marker dictionary 718, anddetermining that the star matches one of the symbols in the markerdictionary 718 that corresponds with the first marker 304A. Similarly,the tracking system 100 may identify the second marker 304B byidentifying a triangle on the marker grid 702, comparing the triangle tothe symbols in the marker dictionary 718, and determining that thetriangle matches one of the symbols in the marker dictionary 718 thatcorresponds with the second marker 304B. The tracking system 100 mayrepeat this process for any other identified markers 304 in the frame302.

In another embodiment, the marker grid 702 may comprise markers 304 thatcontain text. In this example, each marker 304 can be uniquelyidentified based on its text. This configuration allows the trackingsystem 100 to identify markers 304 in the frame 302 by using textrecognition or optical character recognition techniques on the frame302. In this case, the tracking system 100 may use a marker dictionary718 that comprises a plurality of predefined words that are eachassociated with a marker 304 on the marker grid 702. For example, thetracking system 100 may perform text recognition to identify text withthe frame 302. The tracking system 100 may then compare the identifiedtext to words in the marker dictionary 718. Here, the tracking system100 checks whether the identified text matched any of the known textthat corresponds with a marker 304 on the marker grid 702. The trackingsystem 100 may discard any text that does not match any words in themarker dictionary 718. When the tracking system 100 identifies text thatmatches a word in the marker dictionary 718, the tracking system 100 mayidentify the marker 304 that corresponds with the identified text. Forinstance, the tracking system 100 may determine that the identified textmatches the text associated with the first marker 304A. The trackingsystem 100 may identify the second marker 304B and any other markers 304on the marker grid 702 using a similar process.

Returning to FIG. 6 at step 610, the tracking system 100 determines anumber of identified markers 304 within the frame 302. Here, trackingsystem 100 counts the number of markers 304 that were detected withinthe frame 302. Referring to the example in FIG. 7, the tracking system100 detects five markers 304 within the frame 302.

Returning to FIG. 6 at step 614, the tracking system 100 determineswhether the number of identified markers 304 is greater than or equal toa predetermined threshold value. The tracking system 100 may compare thenumber of identified markers 304 to the predetermined threshold valueusing a process similar to the process described in step 210 of FIG. 2.The tracking system 100 returns to step 606 in response to determiningthat the number of identified markers 304 is less than the predeterminedthreshold value. In this case, the tracking system 100 returns to step606 to capture another frame 302 of the space 102 using the same sensor108 to try to detect more markers 304. Here, the tracking system 100tries to obtain a new frame 302 that includes a number of markers 304that is greater than or equal to the predetermined threshold value. Forexample, the tracking system 100 may receive new frame 302 of the space102 after an operator repositions the marker grid 702 within the space102. As another example, the tracking system 100 may receive new frame302 after lighting conditions have been changed to improve thedetectability of the markers 304 within the frame 302. In otherexamples, the tracking system 100 may receive new frame 302 after anykind of change that improves the detectability of the markers 304 withinthe frame 302.

The tracking system 100 proceeds to step 614 in response to determiningthat the number of identified markers 304 is greater than or equal tothe predetermined threshold value. Once the tracking system 100identifies a suitable number of markers 304 on the marker grid 702, thetracking system 100 then determines a pixel location 402 for each of theidentified markers 304. Each marker 304 may occupy multiple pixels inthe frame 302. This means that for each marker 304, the tracking system100 determines which pixel location 402 in the frame 302 correspondswith its (x,y) coordinate 306 in the global plane 104. In oneembodiment, the tracking system 100 using bounding boxes 708 to narrowor restrict the search space when trying to identify pixel location 402for markers 304. A bounding box 708 is a defined area or region withinthe frame 302 that contains a marker 304. For example, a bounding box708 may be defined as a set of pixels or a range of pixels of the frame302 that comprise a marker 304.

At step 614, the tracking system 100 identifies bounding boxes 708 formarkers 304 within the frame 302. In one embodiment, the tracking system100 identifies a plurality of pixels in the frame 302 that correspondwith a marker 304 and then defines a bounding box 708 that encloses thepixels corresponding with the marker 304. The tracking system 100 mayrepeat this process for each of the markers 304. Returning to theexample in FIG. 7, the tracking system 100 may identify a first boundingbox 708A for the first marker 304A, a second bounding box 708B for thesecond marker 304B, and bounding boxes 708 for any other identifiedmarkers 304 within the frame 302.

In another embodiment, the tracking system may employ text or characterrecognition to identify the first marker 304A when the first marker 304Acomprises text. For example, the tracking system 100 may use textrecognition to identify pixels with the frame 302 that comprises a wordcorresponding with a marker 304. The tracking system 100 may then definea bounding box 708 that encloses the pixels corresponding with theidentified word. In other embodiments, the tracking system 100 mayemploy any other suitable image processing technique for identifyingbounding boxes 708 for the identified markers 304.

Returning to FIG. 6 at step 616, the tracking system 100 identifies apixel 710 within each bounding box 708 that corresponds with a pixellocation 402 in the frame 302 for a marker 304. As discussed above, eachmarker 304 may occupy multiple pixels in the frame 302 and the trackingsystem 100 determines which pixel 710 in the frame 302 corresponds withthe pixel location 402 for an (x,y) coordinate 306 in the global plane104. In one embodiment, each marker 304 comprises a light source.Examples of light sources include, but are not limited to, lightemitting diodes (LEDs), infrared (IR) LEDs, incandescent lights, or anyother suitable type of light source. In this configuration, a pixel 710corresponds with a light source for a marker 304. In another embodiment,each marker 304 may comprise a detectable feature that is unique to eachmarker 304. For example, each marker 304 may comprise a unique colorthat is associated with the marker 304. As another example, each marker304 may comprise a unique symbol or pattern that is associated with themarker 304. In this configuration, a pixel 710 corresponds with thedetectable feature for the marker 304. Continuing with the previousexample, the tracking system 100 identifies a first pixel 710A for thefirst marker 304, a second pixel 710B for the second marker 304, andpixels 710 for any other identified markers 304.

At step 618, the tracking system 100 determines pixel locations 402within the frame 302 for each of the identified pixels 710. For example,the tracking system 100 may identify a first pixel row and a first pixelcolumn of the frame 302 that corresponds with the first pixel 710A.Similarly, the tracking system 100 may identify a pixel row and a pixelcolumn in the frame 302 for each of the identified pixels 710.

The tracking system 100 generates a homography 118 for the sensor 108after the tracking system 100 determines (x,y) coordinates 306 in theglobal plane 104 and pixel locations 402 in the frame 302 for each ofthe identified markers 304. At step 620, the tracking system 100generates a homography 118 for the sensor 108 based on the pixellocations 402 of identified markers 304 in the frame 302 of the sensor108 and the (x,y) coordinate 306 of the identified markers 304 in theglobal plane 104. In one embodiment, the tracking system 100 correlatesthe pixel location 402 for each of the identified markers 304 with itscorresponding (x,y) coordinate 306. Continuing with the example in FIG.7, the tracking system 100 associates the first pixel location 402 forthe first marker 304A with the second (x,y) coordinate 306B for thefirst marker 304A. The tracking system 100 also associates the secondpixel location 402 for the second marker 304B with the third (x,y)location 306C for the second marker 304B. The tracking system 100 mayrepeat this process for all of the identified markers 304.

The tracking system 100 then determines a relationship between the pixellocations 402 of identified markers 304 with the frame 302 of the sensor108 and the (x,y) coordinate 306 of the identified markers 304 in theglobal plane 104 to generate a homography 118 for the sensor 108. Thegenerated homography 118 allows the tracking system 100 to map pixellocations 402 in a frame 302 from the sensor 108 to (x,y) coordinates306 in the global plane 104. The generated homography 118 is similar tothe homography described in FIGS. 5A and 5B. Once the tracking system100 generates the homography 118 for the sensor 108, the tracking system100 stores an association between the sensor 108 and the generatedhomography 118 in memory (e.g. memory 3804).

The tracking system 100 may repeat the process described above togenerate and associate homographies 118 with other sensors 108. Themarker grid 702 may be moved or repositioned within the space 108 togenerate a homography 118 for another sensor 108. For example, anoperator may reposition the marker grid 702 to allow another sensor 108to view the markers 304 on the marker grid 702. As an example, thetracking system 100 may receive a second frame 302 from a second sensor108. In this example, the second frame 302 comprises the first marker304A and the second marker 304B. The tracking system 100 may determine athird pixel location 402 in the second frame 302 for the first marker304A and a fourth pixel location 402 in the second frame 302 for thesecond marker 304B. The tracking system 100 may then generate a secondhomography 118 based on the third pixel location 402 in the second frame302 for the first marker 304A, the fourth pixel location 402 in thesecond frame 302 for the second marker 304B, the (x,y) coordinate 306Bin the global plane 104 for the first marker 304A, the (x,y) coordinate306C in the global plane 104 for the second marker 304B, and pixellocations 402 and (x,y) coordinates 306 for other markers 304. Thesecond homography 118 comprises coefficients that translate betweenpixel locations 402 in the second frame 302 and physical locations (e.g.(x,y) coordinates 306) in the global plane 104. The coefficients of thesecond homography 118 are different from the coefficients of thehomography 118 that is associated with the first sensor 108. In otherwords, each sensor 108 is uniquely associated with a homography 118 thatmaps pixel locations 402 from the sensor 108 to physical locations inthe global plane 104. This process uniquely associates a homography 118to a sensor 108 based on the physical location (e.g. (x,y) coordinate306) of the sensor 108 in the global plane 104.

Shelf Position Calibration

FIG. 8 is a flowchart of an embodiment of a shelf position calibrationmethod 800 for the tracking system 100. The tracking system 100 mayemploy method 800 to periodically check whether a rack 112 or sensor 108has moved within the space 102. For example, a rack 112 may beaccidently bumped or moved by a person which causes the rack's 112position to move with respect to the global plane 104. As anotherexample, a sensor 108 may come loose from its mounting structure whichcauses the sensor 108 to sag or move from its original location. Anychanges in the position of a rack 112 and/or a sensor 108 after thetracking system 100 has been calibrated will reduce the accuracy andperformance of the tracking system 100 when tracking objects within thespace 102. The tracking system 100 employs method 800 to detect wheneither a rack 112 or a sensor 108 has moved and then recalibrates itselfbased on the new position of the rack 112 or sensor 108.

A sensor 108 may be positioned within the space 102 such that frames 302captured by the sensor 108 will include one or more shelf markers 906that are located on a rack 112. A shelf marker 906 is an object that ispositioned on a rack 112 that can be used to determine a location (e.g.an (x,y) coordinate 306 and a pixel location 402) for the rack 112. Thetracking system 100 is configured to store the pixel locations 402 andthe (x,y) coordinates 306 of the shelf markers 906 that are associatedwith frames 302 from a sensor 108. In one embodiment, the pixellocations 402 and the (x,y) coordinates 306 of the shelf markers 906 maybe determined using a process similar to the process described in FIG.2. In another embodiment, the pixel locations 402 and the (x,y)coordinates 306 of the shelf markers 906 may be provided by an operatoras an input to the tracking system 100.

A shelf marker 906 may be an object similar to the marker 304 describedin FIGS. 2-7. In some embodiments, each shelf marker 906 on a rack 112is unique from other shelf markers 906 on the rack 112. This featureallows the tracking system 100 to determine an orientation of the rack112. Referring to the example in FIG. 9, each shelf marker 906 is aunique shape that identifies a particular portion of the rack 112. Inthis example, the tracking system 100 may associate a first shelf marker906A and a second shelf marker 906B with a front of the rack 112.Similarly, the tracking system 100 may also associate a third shelfmarker 906C and a fourth shelf marker 906D with a back of the rack 112.In other examples, each shelf marker 906 may have any other uniquelyidentifiable features (e.g. color or patterns) that can be used toidentify a shelf marker 906.

Returning to FIG. 8 at step 802, the tracking system 100 receives afirst frame 302A from a first sensor 108. Referring to FIG. 9 as anexample, the first sensor 108 captures the first frame 302A whichcomprises at least a portion of a rack 112 within the global plane 104for the space 102.

Returning to FIG. 8 at step 804, the tracking system 100 identifies oneor more shelf markers 906 within the first frame 302A. Returning againto the example in FIG. 9, the rack 112 comprises four shelf markers 906.In one embodiment, the tracking system 100 may use object detection toidentify shelf markers 906 within the first frame 302A. For example, thetracking system 100 may search the first frame 302A for known features(e.g. shapes, patterns, colors, text, etc.) that correspond with a shelfmarker 906. In this example, the tracking system 100 may identify ashape (e.g. a star) in the first frame 302A that corresponds with afirst shelf marker 906A. In other embodiments, the tracking system 100may use any other suitable technique to identify a shelf marker 906within the first frame 302A. The tracking system 100 may identify anynumber of shelf markers 906 that are present in the first frame 302A.

Once the tracking system 100 identifies one or more shelf markers 906that are present in the first frame 302A of the first sensor 108, thetracking system 100 then determines their pixel locations 402 in thefirst frame 302A so they can be compared to expected pixel locations 402for the shelf markers 906. Returning to FIG. 8 at step 806, the trackingsystem 100 determines current pixel locations 402 for the identifiedshelf markers 906 in the first frame 302A. Returning to the example inFIG. 9, the tracking system 100 determines a first current pixellocation 402A for the shelf marker 906 within the first frame 302A. Thefirst current pixel location 402A comprises a first pixel row and firstpixel column where the shelf marker 906 is located within the firstframe 302A.

Returning to FIG. 8 at step 808, the tracking system 100 determineswhether the current pixel locations 402 for the shelf markers 906 matchthe expected pixel locations 402 for the shelf markers 906 in the firstframe 302A. Returning to the example in FIG. 9, the tracking system 100determines whether the first current pixel location 402A matches a firstexpected pixel location 402 for the shelf marker 906. As discussedabove, when the tracking system 100 is initially calibrated, thetracking system 100 stores pixel location information 908 that comprisesexpected pixel locations 402 within the first frame 302A of the firstsensor 108 for shelf markers 906 of a rack 112. The tracking system 100uses the expected pixel locations 402 as reference points to determinewhether the rack 112 has moved. By comparing the expected pixel location402 for a shelf marker 906 with its current pixel location 402, thetracking system 100 can determine whether there are any discrepanciesthat would indicate that the rack 112 has moved.

The tracking system 100 may terminate method 800 in response todetermining that the current pixel locations 402 for the shelf markers906 in the first frame 302A match the expected pixel location 402 forthe shelf markers 906. In this case, the tracking system 100 determinesthat neither the rack 112 nor the first sensor 108 has moved since thecurrent pixel locations 402 match the expected pixel locations 402 forthe shelf marker 906.

The tracking system 100 proceeds to step 810 in response to adetermination at step 808 that one or more current pixel locations 402for the shelf markers 906 does not match an expected pixel location 402for the shelf markers 906. For example, the tracking system 100 maydetermine that the first current pixel location 402A does not match thefirst expected pixel location 402 for the shelf marker 906. In thiscase, the tracking system 100 determines that rack 112 and/or the firstsensor 108 has moved since the first current pixel location 402A doesnot match the first expected pixel location 402 for the shelf marker906. Here, the tracking system 100 proceeds to step 810 to identifywhether the rack 112 has moved or the first sensor 108 has moved.

At step 810, the tracking system 100 receives a second frame 302B from asecond sensor 108. The second sensor 108 is adjacent to the first sensor108 and has at least a partially overlapping field of view with thefirst sensor 108. The first sensor 108 and the second sensor 108 ispositioned such that one or more shelf markers 906 are observable byboth the first sensor 108 and the second sensor 108. In thisconfiguration, the tracking system 100 can use a combination ofinformation from the first sensor 108 and the second sensor 108 todetermine whether the rack 112 has moved or the first sensor 108 hasmoved. Returning to the example in FIG. 9, the second frame 304Bcomprises the first shelf marker 906A, the second shelf marker 906B, thethird shelf marker 906C, and the fourth shelf marker 906D of the rack112.

Returning to FIG. 8 at step 812, the tracking system 100 identifies theshelf markers 906 that are present within the second frame 302B from thesecond sensor 108. The tracking system 100 may identify shelf markers906 using a process similar to the process described in step 804.Returning again to the example in FIG. 9, tracking system 100 may searchthe second frame 302B for known features (e.g. shapes, patterns, colors,text, etc.) that correspond with a shelf marker 906. For example, thetracking system 100 may identify a shape (e.g. a star) in the secondframe 302B that corresponds with the first shelf marker 906A.

Once the tracking system 100 identifies one or more shelf markers 906that are present in the second frame 302B of the second sensor 108, thetracking system 100 then determines their pixel locations 402 in thesecond frame 302B so they can be compared to expected pixel locations402 for the shelf markers 906. Returning to FIG. 8 at step 814, thetracking system 100 determines current pixel locations 402 for theidentified shelf markers 906 in the second frame 302B. Returning to theexample in FIG. 9, the tracking system 100 determines a second currentpixel location 402B for the shelf marker 906 within the second frame302B. The second current pixel location 402B comprises a second pixelrow and a second pixel column where the shelf marker 906 is locatedwithin the second frame 302B from the second sensor 108.

Returning to FIG. 8 at step 816, tracking system 100 determines whetherthe current pixel locations 402 for the shelf markers 906 match theexpected pixel locations 402 for the shelf markers 906 in the secondframe 302B. Returning to the example in FIG. 9, the tracking system 100determines whether the second current pixel location 402B matches asecond expected pixel location 402 for the shelf marker 906. Similar toas discussed above in step 808, the tracking system 100 stores pixellocation information 908 that comprises expected pixel locations 402within the second frame 302B of the second sensor 108 for shelf markers906 of a rack 112 when the tracking system 100 is initially calibrated.By comparing the second expected pixel location 402 for the shelf marker906 to its second current pixel location 402B, the tracking system 100can determine whether the rack 112 has moved or whether the first sensor108 has moved.

The tracking system 100 determines that the rack 112 has moved when thecurrent pixel location 402 and the expected pixel location 402 for oneor more shelf markers 906 do not match for multiple sensors 108. When arack 112 moves within the global plane 104, the physical location of theshelf markers 906 moves which causes the pixel locations 402 for theshelf markers 906 to also move with respect to any sensors 108 viewingthe shelf markers 906. This means that the tracking system 100 canconclude that the rack 112 has moved when multiple sensors 108 observe amismatch between current pixel locations 402 and expected pixellocations 402 for one or more shelf markers 906.

The tracking system 100 determines that the first sensor 108 has movedwhen the current pixel location 402 and the expected pixel location 402for one or more shelf markers 906 do not match only for the first sensor108. In this case, the first sensor 108 has moved with respect to therack 112 and its shelf markers 906 which causes the pixel locations 402for the shelf markers 906 to move with respect to the first sensor 108.The current pixel locations 402 of the shelf markers 906 will stillmatch the expected pixel locations 402 for the shelf markers 906 forother sensors 108 because the position of these sensors 108 and the rack112 has not changed.

The tracking system proceeds to step 818 in response to determining thatthe current pixel location 402 matches the second expected pixellocation 402 for the shelf marker 906 in the second frame 302B for thesecond sensor 108. In this case, the tracking system 100 determines thatthe first sensor 108 has moved. At step 818, the tracking system 100recalibrates the first sensor 108. In one embodiment, the trackingsystem 100 recalibrates the first sensor 108 by generating a newhomography 118 for the first sensor 108. The tracking system 100 maygenerate a new homography 118 for the first sensor 108 using shelfmarkers 906 and/or other markers 304. The tracking system 100 maygenerate the new homography 118 for the first sensor 108 using a processsimilar to the processes described in FIGS. 2 and/or 6.

As an example, the tracking system 100 may use an existing homography118 that is currently associated with the first sensor 108 to determinephysical locations (e.g. (x,y) coordinates 306) for the shelf markers906. The tracking system 110 may then use the current pixel locations402 for the shelf markers 906 with their determined (x,y) coordinates306 to generate a new homography 118 for first sensor 108. For instance,the tracking system 100 may use an existing homography 118 that isassociated with the first sensor 108 to determine a first (x,y)coordinate 306 in the global plane 104 where a first shelf marker 906 islocated, a second (x,y) coordinate 306 in the global plane 104 where asecond shelf marker 906 is located, and (x,y) coordinates 306 for anyother shelf markers 906. The tracking system 100 may apply the existinghomography 118 for the first sensor 108 to the current pixel location402 for the first shelf marker 906 in the first frame 302A to determinethe first (x,y) coordinate 306 for the first marker 906 using a processsimilar to the process described in FIG. 5A. The tracking system 100 mayrepeat this process for determining (x,y) coordinates 306 for any otheridentified shelf markers 906. Once the tracking system 100 determines(x,y) coordinates 306 for the shelf markers 906 and the current pixellocations 402 in the first frame 302A for the shelf markers 906, thetracking system 100 may then generate a new homography 118 for the firstsensor 108 using this information. For example, the tracking system 100may generate the new homography 118 based on the current pixel location402 for the first marker 906A, the current pixel location 402 for thesecond marker 906B, the first (x,y) coordinate 306 for the first marker906A, the second (x,y) coordinate 306 for the second marker 906B, and(x,y) coordinates 306 and pixel locations 402 for any other identifiedshelf markers 906 in the first frame 302A. The tracking system 100associates the first sensor 108 with the new homography 118. Thisprocess updates the homography 118 that is associated with the firstsensor 108 based on the current location of the first sensor 108.

In another embodiment, the tracking system 100 may recalibrate the firstsensor 108 by updating the stored expected pixel locations for the shelfmarker 906 for the first sensor 108. For example, the tracking system100 may replace the previous expected pixel location 402 for the shelfmarker 906 with its current pixel location 402. Updating the expectedpixel locations 402 for the shelf markers 906 with respect to the firstsensor 108 allows the tracking system 100 to continue to monitor thelocation of the rack 112 using the first sensor 108. In this case, thetracking system 100 can continue comparing the current pixel locations402 for the shelf markers 906 in the first frame 302A for the firstsensor 108 with the new expected pixel locations 402 in the first frame302A.

At step 820, the tracking system 100 sends a notification that indicatesthat the first sensor 108 has moved. Examples of notifications include,but are not limited to, text messages, short message service (SMS)messages, multimedia messaging service (MMS) messages, pushnotifications, application popup notifications, emails, or any othersuitable type of notifications. For example, the tracking system 100 maysend a notification indicating that the first sensor 108 has moved to aperson associated with the space 102. In response to receiving thenotification, the person may inspect and/or move the first sensor 108back to its original location.

Returning to step 816, the tracking system 100 proceeds to step 822 inresponse to determining that the current pixel location 402 does notmatch the expected pixel location 402 for the shelf marker 906 in thesecond frame 302B. In this case, the tracking system 100 determines thatthe rack 112 has moved. At step 822, the tracking system 100 updates theexpected pixel location information 402 for the first sensor 108 and thesecond sensor 108. For example, the tracking system 100 may replace theprevious expected pixel location 402 for the shelf marker 906 with itscurrent pixel location 402 for both the first sensor 108 and the secondsensor 108. Updating the expected pixel locations 402 for the shelfmarkers 906 with respect to the first sensor 108 and the second sensor108 allows the tracking system 100 to continue to monitor the locationof the rack 112 using the first sensor 108 and the second sensor 108. Inthis case, the tracking system 100 can continue comparing the currentpixel locations 402 for the shelf markers 906 for the first sensor 108and the second sensor 108 with the new expected pixel locations 402.

At step 824, the tracking system 100 sends a notification that indicatesthat the rack 112 has moved. For example, the tracking system 100 maysend a notification indicating that the rack 112 has moved to a personassociated with the space 102. In response to receiving thenotification, the person may inspect and/or move the rack 112 back toits original location. The tracking system 100 may update the expectedpixel locations 402 for the shelf markers 906 again once the rack 112 ismoved back to its original location.

Object Tracking Handoff

FIG. 10 is a flowchart of an embodiment of a tracking hand off method1000 for the tracking system 100. The tracking system 100 may employmethod 1000 to hand off tracking information for an object (e.g. aperson) as it moves between the fields of view of adjacent sensors 108.For example, the tracking system 100 may track the position of people(e.g. shoppers) as they move around within the interior of the space102. Each sensor 108 has a limited field of view which means that eachsensor 108 can only track the position of a person within a portion ofthe space 102. The tracking system 100 employs a plurality of sensors108 to track the movement of a person within the entire space 102. Eachsensor 108 operates independent from one another which means that thetracking system 100 keeps track of a person as they move from the fieldof view of one sensor 108 into the field of view of an adjacent sensor108.

The tracking system 100 is configured such that an object identifier1118 (e.g. a customer identifier) is assigned to each person as theyenter the space 102. The object identifier 1118 may be used to identifya person and other information associated with the person. Examples ofobject identifiers 1118 include, but are not limited to, names, customeridentifiers, alphanumeric codes, phone numbers, email addresses, or anyother suitable type of identifier for a person or object. In thisconfiguration, the tracking system 100 tracks a person's movement withinthe field of view of a first sensor 108 and then hands off trackinginformation (e.g. an object identifier 1118) for the person as it entersthe field of view of a second adjacent sensor 108.

In one embodiment, the tracking system 100 comprises adjacency lists1114 for each sensor 108 that identifies adjacent sensors 108 and thepixels within the frame 302 of the sensor 108 that overlap with theadjacent sensors 108. Referring to the example in FIG. 11, a firstsensor 108 and a second sensor 108 have partially overlapping fields ofview. This means that a first frame 302A from the first sensor 108partially overlaps with a second frame 302B from the second sensor 108.The pixels that overlap between the first frame 302A and the secondframe 302B are referred to as an overlap region 1110. In this example,the tracking system 100 comprises a first adjacency list 1114A thatidentifies pixels in the first frame 302A that correspond with theoverlap region 1110 between the first sensor 108 and the second sensor108. For example, the first adjacency list 1114A may identify a range ofpixels in the first frame 302A that correspond with the overlap region1110. The first adjacency list 114A may further comprise informationabout other overlap regions between the first sensor 108 and otheradjacent sensors 108. For instance, a third sensor 108 may be configuredto capture a third frame 302 that partially overlaps with the firstframe 302A. In this case, the first adjacency list 1114A will furthercomprise information that identifies pixels in the first frame 302A thatcorrespond with an overlap region between the first sensor 108 and thethird sensor 108. Similarly, the tracking system 100 may furthercomprise a second adjacency list 1114B that is associated with thesecond sensor 108. The second adjacency list 1114B identifies pixels inthe second frame 302B that correspond with the overlap region 1110between the first sensor 108 and the second sensor 108. The secondadjacency list 1114B may further comprise information about otheroverlap regions between the second sensor 108 and other adjacent sensors108. In FIG. 11, the second tracking list 1112B is shown as a separatedata structure from the first tracking list 1112A, however, the trackingsystem 100 may use a single data structure to store tracking listinformation that is associated with multiple sensors 108.

Once the first person 1106 enters the space 102, the tracking system 100will track the object identifier 1118 associated with the first person1106 as well as pixel locations 402 in the sensors 108 where the firstperson 1106 appears in a tracking list 1112. For example, the trackingsystem 100 may track the people within the field of view of a firstsensor 108 using a first tracking list 1112A, the people within thefield of view of a second sensor 108 using a second tracking list 1112B,and so on. In this example, the first tracking list 1112A comprisesobject identifiers 1118 for people being tracked using the first sensor108. The first tracking list 1112A further comprises pixel locationinformation that indicates the location of a person within the firstframe 302A of the first sensor 108. In some embodiments, the firsttracking list 1112A may further comprise any other suitable informationassociated with a person being tracked by the first sensor 108. Forexample, the first tracking list 1112A may identify (x,y) coordinates306 for the person in the global plane 104, previous pixel locations 402within the first frame 302A for a person, and/or a travel direction 1116for a person. For instance, the tracking system 100 may determine atravel direction 1116 for the first person 1106 based on their previouspixel locations 402 within the first frame 302A and may store thedetermined travel direction 1116 in the first tracking list 1112A. Inone embodiment, the travel direction 1116 may be represented as a vectorwith respect to the global plane 104. In other embodiments, the traveldirection 1116 may be represented using any other suitable format.

Returning to FIG. 10 at step 1002, the tracking system 100 receives afirst frame 302A from a first sensor 108. Referring to FIG. 11 as anexample, the first sensor 108 captures an image or frame 302A of aglobal plane 104 for at least a portion of the space 102. In thisexample, the first frame 1102 comprises a first object (e.g. a firstperson 1106) and a second object (e.g. a second person 1108). In thisexample, the first frame 302A captures the first person 1106 and thesecond person 1108 as they move within the space 102.

Returning to FIG. 10 at step 1004, the tracking system 100 determines afirst pixel location 402A in the first frame 302A for the first person1106. Here, the tracking system 100 determines the current location forthe first person 1106 within the first frame 302A from the first sensor108. Continuing with the example in FIG. 11, the tracking system 100identifies the first person 1106 in the first frame 302A and determinesa first pixel location 402A that corresponds with the first person 1106.In a given frame 302, the first person 1106 is represented by acollection of pixels within the frame 302. Referring to the example inFIG. 11, the first person 1106 is represented by a collection of pixelsthat show an overhead view of the first person 1106. The tracking system100 associates a pixel location 402 with the collection of pixelsrepresenting the first person 1106 to identify the current location ofthe first person 1106 within a frame 302. In one embodiment, the pixellocation 402 of the first person 1106 may correspond with the head ofthe first person 1106. In this example, the pixel location 402 of thefirst person 1106 may be located at about the center of the collectionof pixels that represent the first person 1106. As another example, thetracking system 100 may determine a bounding box 708 that encloses thecollection of pixels in the first frame 302A that represent the firstperson 1106. In this example, the pixel location 402 of the first person1106 may located at about the center of the bounding box 708.

As another example, the tracking system 100 may use object detection orcontour detection to identify the first person 1106 within the firstframe 302A. In this example, the tracking system 100 may identify one ormore features for the first person 1106 when they enter the space 102.The tracking system 100 may later compare the features of a person inthe first frame 302A to the features associated with the first person1106 to determine if the person is the first person 1106. In otherexamples, the tracking system 100 may use any other suitable techniquesfor identifying the first person 1106 within the first frame 302A. Thefirst pixel location 402A comprises a first pixel row and a first pixelcolumn that corresponds with the current location of the first person1106 within the first frame 302A.

Returning to FIG. 10 at step 1006, the tracking system 100 determinesthe object is within the overlap region 1110 between the first sensor108 and the second sensor 108. Returning to the example in FIG. 11, thetracking system 100 may compare the first pixel location 402A for thefirst person 1106 to the pixels identified in the first adjacency list1114A that correspond with the overlap region 1110 to determine whetherthe first person 1106 is within the overlap region 1110. The trackingsystem 100 may determine that the first object 1106 is within theoverlap region 1110 when the first pixel location 402A for the firstobject 1106 matches or is within a range of pixels identified in thefirst adjacency list 1114A that corresponds with the overlap region1110. For example, the tracking system 100 may compare the pixel columnof the pixel location 402A with a range of pixel columns associated withthe overlap region 1110 and the pixel row of the pixel location 402Awith a range of pixel rows associated with the overlap region 1110 todetermine whether the pixel location 402A is within the overlap region1110. In this example, the pixel location 402A for the first person 1106is within the overlap region 1110.

At step 1008, the tracking system 100 applies a first homography 118 tothe first pixel location 402A to determine a first (x,y) coordinate 306in the global plane 104 for the first person 1106. The first homography118 is configured to translate between pixel locations 402 in the firstframe 302A and (x,y) coordinates 306 in the global plane 104. The firsthomography 118 is configured similar to the homography 118 described inFIGS. 2-5B. As an example, the tracking system 100 may identify thefirst homography 118 that is associated with the first sensor 108 andmay use matrix multiplication between the first homography 118 and thefirst pixel location 402A to determine the first (x,y) coordinate 306 inthe global plane 104.

At step 1010, the tracking system 100 identifies an object identifier1118 for the first person 1106 from the first tracking list 1112Aassociated with the first sensor 108. For example, the tracking system100 may identify an object identifier 1118 that is associated with thefirst person 1106. At step 1012, the tracking system 100 stores theobject identifier 1118 for the first person 1106 in a second trackinglist 1112B associated with the second sensor 108. Continuing with theprevious example, the tracking system 100 may store the objectidentifier 1118 for the first person 1106 in the second tracking list1112B. Adding the object identifier 1118 for the first person 1106 tothe second tracking list 1112B indicates that the first person 1106 iswithin the field of view of the second sensor 108 and allows thetracking system 100 to begin tracking the first person 1106 using thesecond sensor 108.

Once the tracking system 100 determines that the first person 1106 hasentered the field of view of the second sensor 108, the tracking system100 then determines where the first person 1106 is located in the secondframe 302B of the second sensor 108 using a homography 118 that isassociated with the second sensor 108. This process identifies thelocation of the first person 1106 with respect to the second sensor 108so they can be tracked using the second sensor 108. At step 1014, thetracking system 100 applies a homography 118 that is associated with thesecond sensor 108 to the first (x,y) coordinate 306 to determine asecond pixel location 402B in the second frame 302B for the first person1106. The homography 118 is configured to translate between pixellocations 402 in the second frame 302B and (x,y) coordinates 306 in theglobal plane 104. The homography 118 is configured similar to thehomography 118 described in FIGS. 2-5B. As an example, the trackingsystem 100 may identify the homography 118 that is associated with thesecond sensor 108 and may use matrix multiplication between the inverseof the homography 118 and the first (x,y) coordinate 306 to determinethe second pixel location 402B in the second frame 302B.

At step 1016, the tracking system 100 stores the second pixel location402B with the object identifier 1118 for the first person 1106 in thesecond tracking list 1112B. In some embodiments, the tracking system 100may store additional information associated with the first person 1106in the second tracking list 1112B. For example, the tracking system 100may be configured to store a travel direction 1116 or any other suitabletype of information associated with the first person 1106 in the secondtracking list 1112B. After storing the second pixel location 402B in thesecond tracking list 1112B, the tracking system 100 may begin trackingthe movement of the person within the field of view of the second sensor108.

The tracking system 100 will continue to track the movement of the firstperson 1106 to determine when they completely leave the field of view ofthe first sensor 108. At step 1018, the tracking system 100 receives anew frame 302 from the first sensor 108. For example, the trackingsystem 100 may periodically receive additional frames 302 from the firstsensor 108. For instance, the tracking system 100 may receive a newframe 302 from the first sensor 108 every millisecond, every second,every five second, or at any other suitable time interval.

At step 1020, the tracking system 100 determines whether the firstperson 1106 is present in the new frame 302. If the first person 1106 ispresent in the new frame 302, then this means that the first person 1106is still within the field of view of the first sensor 108 and thetracking system 100 should continue to track the movement of the firstperson 1106 using the first sensor 108. If the first person 1106 is notpresent in the new frame 302, then this means that the first person 1106has left the field of view of the first sensor 108 and the trackingsystem 100 no longer needs to track the movement of the first person1106 using the first sensor 108. The tracking system 100 may determinewhether the first person 1106 is present in the new frame 302 using aprocess similar to the process described in step 1004. The trackingsystem 100 returns to step 1018 to receive additional frames 302 fromthe first sensor 108 in response to determining that the first person1106 is present in the new frame 1102 from the first sensor 108.

The tracking system 100 proceeds to step 1022 in response to determiningthat the first person 1106 is not present in the new frame 302. In thiscase, the first person 1106 has left the field of view for the firstsensor 108 and no longer needs to be tracked using the first sensor 108.At step 1022, the tracking system 100 discards information associatedwith the first person 1106 from the first tracking list 1112A. Once thetracking system 100 determines that the first person has left the fieldof view of the first sensor 108, then the tracking system 100 can stoptracking the first person 1106 using the first sensor 108 and can freeup resources (e.g. memory resources) that were allocated to tracking thefirst person 1106. The tracking system 100 will continue to track themovement of the first person 1106 using the second sensor 108 until thefirst person 1106 leaves the field of view of the second sensor 108. Forexample, the first person 1106 may leave the space 102 or may transitionto the field of view of another sensor 108.

Shelf Interaction Detection

FIG. 12 is a flowchart of an embodiment of a shelf interaction detectionmethod 1200 for the tracking system 100. The tracking system 100 mayemploy method 1200 to determine where a person is interacting with ashelf of a rack 112. In addition to tracking where people are locatedwithin the space 102, the tracking system 100 also tracks which items1306 a person picks up from a rack 112. As a shopper picks up items 1306from a rack 112, the tracking system 100 identifies and tracks whichitems 1306 the shopper has picked up, so they can be automatically addedto a digital cart 1410 that is associated with the shopper. This processallows items 1306 to be added to the person's digital cart 1410 withouthaving the shopper scan or otherwise identify the item 1306 they pickedup. The digital cart 1410 comprises information about items 1306 theshopper has picked up for purchase. In one embodiment, the digital cart1410 comprises item identifiers and a quantity associated with each itemin the digital cart 1410. For example, when the shopper picks up acanned beverage, an item identifier for the beverage is added to theirdigital cart 1410. The digital cart 1410 will also indicate the numberof the beverages that the shopper has picked up. Once the shopper leavesthe space 102, the shopper will be automatically charged for the items1306 in their digital cart 1410.

In FIG. 13, a side view of a rack 112 is shown from the perspective of aperson standing in front of the rack 112. In this example, the rack 112may comprise a plurality of shelves 1302 for holding and displayingitems 1306. Each shelf 1302 may be partitioned into one or more zones1304 for holding different items 1306. In FIG. 13, the rack 112comprises a first shelf 1302A at a first height and a second shelf 1302Bat a second height. Each shelf 1302 is partitioned into a first zone1304A and a second zone 1304B. The rack 112 may be configured to carry adifferent item 1306 (i.e. items 1306A, 1306B, 1306C, and 1036D) withineach zone 1304 on each shelf 1302. In this example, the rack 112 may beconfigured to carry up to four different types of items 1306. In otherexamples, the rack 112 may comprise any other suitable number of shelves1302 and/or zones 1304 for holding items 1306. The tracking system 100may employ method 1200 to identify which item 1306 a person picks upfrom a rack 112 based on where the person is interacting with the rack112.

Returning to FIG. 12 at step 1202, the tracking system 100 receives aframe 302 from a sensor 108. Referring to FIG. 14 as an example, thesensor 108 captures a frame 302 of at least a portion of the rack 112within the global plane 104 for the space 102. In FIG. 14, an overheadview of the rack 112 and two people standing in front of the rack 112 isshown from the perspective of the sensor 108. The frame 302 comprises aplurality of pixels that are each associated with a pixel location 402for the sensor 108. Each pixel location 402 comprises a pixel row, apixel column, and a pixel value. The pixel row and the pixel columnindicate the location of a pixel within the frame 302 of the sensor 108.The pixel value corresponds with a z-coordinate (e.g. a height) in theglobal plane 104. The z-coordinate corresponds with a distance betweensensor 108 and a surface in the global plane 104.

The frame 302 further comprises one or more zones 1404 that areassociated with zones 1304 of the rack 112. Each zone 1404 in the frame302 corresponds with a portion of the rack 112 in the global plane 104.Referring to the example in FIG. 14, the frame 302 comprises a firstzone 1404A and a second zone 1404B that are associated with the rack112. In this example, the first zone 1404A and the second zone 1404Bcorrespond with the first zone 1304A and the second zone 1304B of therack 112, respectively.

The frame 302 further comprises a predefined zone 1406 that is used as avirtual curtain to detect where a person 1408 is interacting with therack 112. The predefined zone 1406 is an invisible barrier defined bythe tracking system 100 that the person 1408 reaches through to pick upitems 1306 from the rack 112. The predefined zone 1406 is locatedproximate to the one or more zones 1304 of the rack 112. For example,the predefined zone 1406 may be located proximate to the front of theone or more zones 1304 of the rack 112 where the person 1408 would reachto grab for an item 1306 on the rack 112. In some embodiments, thepredefined zone 1406 may at least partially overlap with the first zone1404A and the second zone 1404B.

Returning to FIG. 12 at step 1204, the tracking system 100 identifies anobject within a predefined zone 1406 of the frame 1402. For example, thetracking system 100 may detect that the person's 1408 hand enters thepredefined zone 1406. In one embodiment, the tracking system 100 maycompare the frame 1402 to a previous frame that was captured by thesensor 108 to detect that the person's 1408 hand has entered thepredefined zone 1406. In this example, the tracking system 100 may usedifferences between the frames 302 to detect that the person's 1408 handenters the predefined zone 1406. In other embodiments, the trackingsystem 100 may employ any other suitable technique for detecting whenthe person's 1408 hand has entered the predefined zone 1406.

In one embodiment, the tracking system 100 identifies the rack 112 thatis proximate to the person 1408. Returning to the example in FIG. 14,the tracking system 100 may determine a pixel location 402A in the frame302 for the person 1408. The tracking system 100 may determine a pixellocation 402A for the person 1408 using a process similar to the processdescribed in step 1004 of FIG. 10. The tracking system 100 may use ahomography 118 associated with the sensor 108 to determine an (x,y)coordinate 306 in the global plane 104 for the person 1408. Thehomography 118 is configured to translate between pixel locations 402 inthe frame 302 and (x,y) coordinates 306 in the global plane 104. Thehomography 118 is configured similar to the homography 118 described inFIGS. 2-5B. As an example, the tracking system 100 may identify thehomography 118 that is associated with the sensor 108 and may use matrixmultiplication between the homography 118 and the pixel location 402A ofthe person 1408 to determine an (x,y) coordinate 306 in the global plane104. The tracking system 100 may then identify which rack 112 is closestto the person 1408 based on the person's 1408 (x,y) coordinate 306 inthe global plane 104.

The tracking system 100 may identify an item map 1308 corresponding withthe rack 112 that is closest to the person 1408. In one embodiment, thetracking system 100 comprises an item map 1308 that associates items1306 with particular locations on the rack 112. For example, an item map1308 may comprise a rack identifier and a plurality of item identifiers.Each item identifier is mapped to a particular location on the rack 112.Returning to the example in FIG. 13, a first item 1306A is mapped to afirst location that identifies the first zone 1304A and the first shelf1302A of the rack 112, a second item 1306B is mapped to a secondlocation that identifies the second zone 1304B and the first shelf 1302Aof the rack 112, a third item 1306C is mapped to a third location thatidentifies the first zone 1304A and the second shelf 1302B of the rack112, and a fourth item 1306D is mapped to a fourth location thatidentifies the second zone 1304B and the second shelf 1302B of the rack112.

Returning to FIG. 12 at step 1206, the tracking system 100 determines apixel location 402B in the frame 302 for the object that entered thepredefined zone 1406. Continuing with the previous example, the pixellocation 402B comprises a first pixel row, a first pixel column, and afirst pixel value for the person's 1408 hand. In this example, theperson's 1408 hand is represented by a collection of pixels in thepredefined zone 1406. In one embodiment, the pixel location 402 of theperson's 1408 hand may be located at about the center of the collectionof pixels that represent the person's 1408 hand. In other examples, thetracking system 100 may use any other suitable technique for identifyingthe person's 1408 hand within the frame 302.

Once the tracking system 100 determines the pixel location 402B of theperson's 1408 hand, the tracking system 100 then determines which shelf1302 and zone 1304 of the rack 112 the person 1408 is reaching for. Atstep 1208, the tracking system 100 determines whether the pixel location402B for the object (i.e. the person's 1408 hand) corresponds with afirst zone 1304A of the rack 112. The tracking system 100 uses the pixellocation 402B of the person's 1408 hand to determine which side of therack 112 the person 1408 is reaching into. Here, the tracking system 100checks whether the person is reaching for an item on the left side ofthe rack 112.

Each zone 1304 of the rack 112 is associated with a plurality of pixelsin the frame 302 that can be used to determine where the person 1408 isreaching based on the pixel location 402B of the person's 1408 hand.Continuing with the example in FIG. 14, the first zone 1304A of the rack112 corresponds with the first zone 1404A which is associated with afirst range of pixels 1412 in the frame 302. Similarly, the second zone1304B of the rack 112 corresponds with the second zone 1404B which isassociated with a second range of pixels 1414 in the frame 302. Thetracking system 100 may compare the pixel location 402B of the person's1408 hand to the first range of pixels 1412 to determine whether thepixel location 402B corresponds with the first zone 1304A of the rack112. In this example, the first range of pixels 1412 corresponds with arange of pixel columns in the frame 302. In other examples, the firstrange of pixels 1412 may correspond with a range of pixel rows or acombination of pixel row and columns in the frame 302.

In this example, the tracking system 100 compares the first pixel columnof the pixel location 402B to the first range of pixels 1412 todetermine whether the pixel location 1410 corresponds with the firstzone 1304A of the rack 112. In other words, the tracking system 100compares the first pixel column of the pixel location 402B to the firstrange of pixels 1412 to determine whether the person 1408 is reachingfor an item 1306 on the left side of the rack 112. In FIG. 14, the pixellocation 402B for the person's 1408 hand does not correspond with thefirst zone 1304A of the rack 112. The tracking system 100 proceeds tostep 1210 in response to determining that the pixel location 402B forthe object corresponds with the first zone 1304A of the rack 112. Atstep 1210, the tracking system 100 identifies the first zone 1304A ofthe rack 112 based on the pixel location 402B for the object thatentered the predefined zone 1406. In this case, the tracking system 100determines that the person 1408 is reaching for an item on the left sideof the rack 112.

Returning to step 1208, the tracking system 100 proceeds to step 1212 inresponse to determining that the pixel location 402B for the object thatentered the predefined zone 1406 does not correspond with the first zone1304B of the rack 112. At step 1212, the tracking system 100 identifiesthe second zone 1304B of the rack 112 based on the pixel location 402Bof the object that entered the predefined zone 1406. In this case, thetracking system 100 determines that the person 1408 is reaching for anitem on the right side of the rack 112.

In other embodiments, the tracking system 100 may compare the pixellocation 402B to other ranges of pixels that are associated with otherzones 1304 of the rack 112. For example, the tracking system 100 maycompare the first pixel column of the pixel location 402B to the secondrange of pixels 1414 to determine whether the pixel location 402Bcorresponds with the second zone 1304B of the rack 112. In other words,the tracking system 100 compares the first pixel column of the pixellocation 402B to the second range of pixels 1414 to determine whetherthe person 1408 is reaching for an item 1306 on the right side of therack 112.

Once the tracking system 100 determines which zone 1304 of the rack 112the person 1408 is reaching into, the tracking system 100 thendetermines which shelf 1302 of the rack 112 the person 1408 is reachinginto. At step 1214, the tracking system 100 identifies a pixel value atthe pixel location 402B for the object that entered the predefined zone1406. The pixel value is a numeric value that corresponds with az-coordinate or height in the global plane 104 that can be used toidentify which shelf 1302 the person 1408 was interacting with. Thepixel value can be used to determine the height the person's 1408 handwas at when it entered the predefined zone 1406 which can be used todetermine which shelf 1302 the person 1408 was reaching into.

At step 1216, the tracking system 100 determines whether the pixel valuecorresponds with the first shelf 1302A of the rack 112. Returning to theexample in FIG. 13, the first shelf 1302A of the rack 112 correspondswith a first range of z-values or heights 1310A and the second shelf1302B corresponds with a second range of z-values or heights 1310B. Thetracking system 100 may compare the pixel value to the first range ofz-values 1310A to determine whether the pixel value corresponds with thefirst shelf 1302A of the rack 112. As an example, the first range ofz-values 1310A may be a range between 2 meters and 1 meter with respectto the z-axis in the global plane 104. The second range of z-values1310B may be a range between 0.9 meters and 0 meters with respect to thez-axis in the global plane 104. The pixel value may have a value thatcorresponds with 1.5 meters with respect to the z-axis in the globalplane 104. In this example, the pixel value is within the first range ofz-values 1310A which indicates that the pixel value corresponds with thefirst shelf 1302A of the rack 112. In other words, the person's 1408hand was detected at a height that indicates the person 1408 wasreaching for the first shelf 1302A of the rack 112. The tracking system100 proceeds to step 1218 in response to determining that the pixelvalue corresponds with the first shelf of the rack 112. At step 1218,the tracking system 100 identifies the first shelf 1302A of the rack 112based on the pixel value.

Returning to step 1216, the tracking system 100 proceeds to step 1220 inresponse to determining that the pixel value does not correspond withthe first shelf 1302A of the rack 112. At step 1220, the tracking system100 identifies the second shelf 1302B of the rack 112 based on the pixelvalue. In other embodiments, the tracking system 100 may compare thepixel value to other z-value ranges that are associated with othershelves 1302 of the rack 112. For example, the tracking system 100 maycompare the pixel value to the second range of z-values 1310B todetermine whether the pixel value corresponds with the second shelf1302B of the rack 112.

Once the tracking system 100 determines which side of the rack 112 andwhich shelf 1302 of the rack 112 the person 1408 is reaching into, thenthe tracking system 100 can identify an item 1306 that corresponds withthe identified location on the rack 112. At step 1222, the trackingsystem 100 identifies an item 1306 based on the identified zone 1304 andthe identified shelf 1302 of the rack 112. The tracking system 100 usesthe identified zone 1304 and the identified shelf 1302 to identify acorresponding item 1306 in the item map 1308. Returning to the examplein FIG. 14, the tracking system 100 may determine that the person 1408is reaching into the right side (i.e. zone 1404B) of the rack 112 andthe first shelf 1302A of the rack 112. In this example, the trackingsystem 100 determines that the person 1408 is reaching for and picked upitem 1306B from the rack 112.

In some instances, multiple people may be near the rack 112 and thetracking system 100 may need to determine which person is interactingwith the rack 112 so that it can add a picked-up item 1306 to theappropriate person's digital cart 1410. Returning to the example in FIG.14, a second person 1420 is also near the rack 112 when the first person1408 is picking up an item 1306 from the rack 112. In this case, thetracking system 100 should assign any picked-up items to the firstperson 1408 and not the second person 1420.

In one embodiment, the tracking system 100 determines which personpicked up an item 1306 based on their proximity to the item 1306 thatwas picked up. For example, the tracking system 100 may determine apixel location 402A in the frame 302 for the first person 1408. Thetracking system 100 may also identify a second pixel location 402C forthe second person 1420 in the frame 302. The tracking system 100 maythen determine a first distance 1416 between the pixel location 402A ofthe first person 1408 and the location on the rack 112 where the item1306 was picked up. The tracking system 100 also determines a seconddistance 1418 between the pixel location 402C of the second person 1420and the location on the rack 112 where the item 1306 was picked up. Thetracking system 100 may then determine that the first person 1408 iscloser to the item 1306 than the second person 1420 when the firstdistance 1416 is less than the second distance 1418. In this example,the tracking system 100 identifies the first person 1408 as the personthat most likely picked up the item 1306 based on their proximity to thelocation on the rack 112 where the item 1306 was picked up. This processallows the tracking system 100 to identify the correct person thatpicked up the item 1306 from the rack 112 before adding the item 1306 totheir digital cart 1410.

Returning to FIG. 12 at step 1224, the tracking system 100 adds theidentified item 1306 to a digital cart 1410 associated with the person1408. In one embodiment, the tracking system 100 uses weight sensors 110to determine a number of items 1306 that were removed from the rack 112.For example, the tracking system 100 may determine a weight decreaseamount on a weight sensor 110 after the person 1408 removes one or moreitems 1306 from the weight sensor 110. The tracking system 100 may thendetermine an item quantity based on the weight decrease amount. Forexample, the tracking system 100 may determine an individual item weightfor the items 1306 that are associated with the weight sensor 110. Forinstance, the weight sensor 110 may be associated with an item 1306 thatthat has an individual weight of sixteen ounces. When the weight sensor110 detects a weight decrease of sixty-four ounces, the weight sensor110 may determine that four of the items 1306 were removed from theweight sensor 110. In other embodiments, the digital cart 1410 mayfurther comprise any other suitable type of information associated withthe person 1408 and/or items 1306 that they have picked up.

Item Assignment Using a Local Zone

FIG. 15 is a flowchart of an embodiment of an item assigning method 1500for the tracking system 100. The tracking system 100 may employ method1500 to detect when an item 1306 has been picked up from a rack 112 andto determine which person to assign the item to using a predefined zone1808 that is associated with the rack 112. In a busy environment, suchas a store, there may be multiple people standing near a rack 112 whenan item is removed from the rack 112. Identifying the correct personthat picked up the item 1306 can be challenging. In this case, thetracking system 100 uses a predefined zone 1808 that can be used toreduce the search space when identifying a person that picks up an item1306 from a rack 112. The predefined zone 1808 is associated with therack 112 and is used to identify an area where a person can pick up anitem 1306 from the rack 112. The predefined zone 1808 allows thetracking system 100 to quickly ignore people are not within an areawhere a person can pick up an item 1306 from the rack 112, for examplebehind the rack 112. Once the item 1306 and the person have beenidentified, the tracking system 100 will add the item to a digital cart1410 that is associated with the identified person.

At step 1502, the tracking system 100 detects a weight decrease on aweight sensor 110. Referring to FIG. 18 as an example, the weight sensor110 is disposed on a rack 112 and is configured to measure a weight forthe items 1306 that are placed on the weight sensor 110. In thisexample, the weight sensor 110 is associated with a particular item1306. The tracking system 100 detects a weight decrease on the weightsensor 110 when a person 1802 removes one or more items 1306 from theweight sensor 110.

Returning to FIG. 15 at step 1504, the tracking system 100 identifies anitem 1306 associated with the weight sensor 110. In one embodiment, thetracking system 100 comprises an item map 1308A that associates items1306 with particular locations (e.g. zones 1304 and/or shelves 1302) andweight sensors 110 on the rack 112. For example, an item map 1308A maycomprise a rack identifier, weight sensor identifiers, and a pluralityof item identifiers. Each item identifier is mapped to a particularweight sensor 110 (i.e. weight sensor identifier) on the rack 112. Thetracking system 100 determines which weight sensor 110 detected a weightdecrease and then identifies the item 1306 or item identifier thatcorresponds with the weight sensor 110 using the item map 1308A.

At step 1506, the tracking system 100 receives a frame 302 of the rack112 from a sensor 108. The sensor 108 captures a frame 302 of at least aportion of the rack 112 within the global plane 104 for the space 102.The frame 302 comprises a plurality of pixels that are each associatedwith a pixel location 402. Each pixel location 402 comprises a pixel rowand a pixel column. The pixel row and the pixel column indicate thelocation of a pixel within the frame 302.

The frame 302 comprises a predefined zone 1808 that is associated withthe rack 112. The predefined zone 1808 is used for identifying peoplethat are proximate to the front of the rack 112 and in a suitableposition for retrieving items 1306 from the rack 112. For example, therack 112 comprises a front portion 1810, a first side portion 1812, asecond side portion 1814, and a back portion 1814. In this example, aperson may be able to retrieve items 1306 from the rack 112 when theyare either in front or to the side of the rack 112. A person is unableto retrieve items 1306 from the rack 112 when they are behind the rack112. In this case, the predefined zone 1808 may overlap with at least aportion of the front portion 1810, the first side portion 1812, and thesecond side portion 1814 of the rack 112 in the frame 1806. Thisconfiguration prevents people that are behind the rack 112 from beingconsidered as a person who picked up an item 1306 from the rack 112. InFIG. 18, the predefined zone 1808 is rectangular. In other examples, thepredefined zone 1808 may be semi-circular or in any other suitableshape.

After the tracking system 100 determines that an item 1306 has beenpicked up from the rack 112, the tracking system 100 then begins toidentify people within the frame 302 that may have picked up the item1306 from the rack 112. At step 1508, the tracking system 100 identifiesa person 1802 within the frame 302. The tracking system 100 may identifya person 1802 within the frame 302 using a process similar to theprocess described in step 1004 of FIG. 10. In other examples, thetracking system 100 may employ any other suitable technique foridentifying a person 1802 within the frame 302.

At step 1510, the tracking system 100 determines a pixel location 402Ain the frame 302 for the identified person 1802. The tracking system 100may determine a pixel location 402A for the identified person 1802 usinga process similar to the process described in step 1004 of FIG. 10. Thepixel location 402A comprises a pixel row and a pixel column thatidentifies the location of the person 1802 in the frame 302 of thesensor 108.

At step 1511, the tracking system 100 applies a homography 118 to thepixel location 402A of the identified person 1802 to determine an (x,y)coordinate 306 in the global plane 104 for the identified person 1802.The homography 118 is configured to translate between pixel locations402 in the frame 302 and (x,y) coordinates 306 in the global plane 104.The homography 118 is configured similar to the homography 118 describedin FIGS. 2-5B. As an example, the tracking system 100 may identify thehomography 118 that is associated with the sensor 108 and may use matrixmultiplication between the homography 118 and the pixel location 402A ofthe identified person 1802 to determine the (x,y) coordinate 306 in theglobal plane 104.

At step 1512, the tracking system 100 determines whether the identifiedperson 1802 is within a predefined zone 1808 associated with the rack112 in the frame 302. Continuing with the example in FIG. 18, thepredefined zone 1808 is associated with a range of (x,y) coordinates 306in the global plane 104. The tracking system 100 may compare the (x,y)coordinate 306 for the identified person 1802 to the range of (x,y)coordinates 306 that are associated with the predefined zone 1808 todetermine whether the (x,y) coordinate 306 for the identified person1802 is within the predefined zone 1808. In other words, the trackingsystem 100 uses the (x,y) coordinate 306 for the identified person 1802to determine whether the identified person 1802 is within an areasuitable for picking up items 1306 from the rack 112. In this example,the (x,y) coordinate 306 for the person 1802 corresponds with a locationin front of the rack 112 and is within the predefined zone 1808 whichmeans that the identified person 1802 is in a suitable area forretrieving items 1306 from the rack 112.

In another embodiment, the predefined zone 1808 is associated with aplurality of pixels (e.g. a range of pixel rows and pixel columns) inthe frame 302. The tracking system 100 may compare the pixel location402A to the pixels associated with the predefined zone 1808 to determinewhether the pixel location 402A is within the predefined zone 1808. Inother words, the tracking system 100 uses the pixel location 402A of theidentified person 1802 to determine whether the identified person 1802is within an area suitable for picking up items 1306 from the rack 112.In this example, the tracking system 100 may compare the pixel column ofthe pixel location 402A with a range of pixel columns associated withthe predefined zone 1808 and the pixel row of the pixel location 402Awith a range of pixel rows associated with the predefined zone 1808 todetermine whether the identified person 1802 is within the predefinedzone 1808. In this example, the pixel location 402A for the person 1802is standing in front of the rack 112 and is within the predefined zone1808 which means that the identified person 1802 is in a suitable areafor retrieving items 1306 from the rack 112.

The tracking system 100 proceeds to step 1514 in response to determiningthat the identified person 1802 is within the predefined zone 1808.Otherwise, the tracking system 100 returns to step 1508 to identifyanother person within the frame 302. In this case, the tracking system100 determines the identified person 1802 is not in a suitable area forretrieving items 1306 from the rack 112, for example the identifiedperson 1802 is standing behind of the rack 112.

In some instances, multiple people may be near the rack 112 and thetracking system 100 may need to determine which person is interactingwith the rack 112 so that it can add a picked-up item 1306 to theappropriate person's digital cart 1410. Returning to the example in FIG.18, a second person 1826 is standing next to the side of rack 112 in theframe 302 when the first person 1802 picks up an item 1306 from the rack112. In this example, the second person 1826 is closer to the rack 112than the first person 1802, however, the tracking system 100 can ignorethe second person 1826 because the pixel location 402B of the secondperson 1826 is outside of the predetermined zone 1808 that is associatedwith the rack 112. For example, the tracking system 100 may identify an(x,y) coordinate 306 in the global plane 104 for the second person 1826and determine that the second person 1826 is outside of the predefinedzone 1808 based on their (x,y) coordinate 306. As another example, thetracking system 100 may identify a pixel location 402B within the frame302 for the second person 1826 and determine that the second person 1826is outside of the predefined zone 1808 based on their pixel location402B.

As another example, the frame 302 further comprises a third person 1832standing near the rack 112. In this case, the tracking system 100determines which person picked up the item 1306 based on their proximityto the item 1306 that was picked up. For example, the tracking system100 may determine an (x,y) coordinate 306 in the global plane 104 forthe third person 1832. The tracking system 100 may then determine afirst distance 1828 between the (x,y) coordinate 306 of the first person1802 and the location on the rack 112 where the item 1306 was picked up.The tracking system 100 also determines a second distance 1830 betweenthe (x,y) coordinate 306 of the third person 1832 and the location onthe rack 112 where the item 1306 was picked up. The tracking system 100may then determine that the first person 1802 is closer to the item 1306than the third person 1832 when the first distance 1828 is less than thesecond distance 1830. In this example, the tracking system 100identifies the first person 1802 as the person that most likely pickedup the item 1306 based on their proximity to the location on the rack112 where the item 1306 was picked up. This process allows the trackingsystem 100 to identify the correct person that picked up the item 1306from the rack 112 before adding the item 1306 to their digital cart1410.

As another example, the tracking system 100 may determine a pixellocation 402C in the frame 302 for a third person 1832. The trackingsystem 100 may then determine the first distance 1828 between the pixellocation 402A of the first person 1802 and the location on the rack 112where the item 1306 was picked up. The tracking system 100 alsodetermines the second distance 1830 between the pixel location 402C ofthe third person 1832 and the location on the rack 112 where the item1306 was picked up.

Returning to FIG. 15 at step 1514, the tracking system 100 adds the item1306 to a digital cart 1410 that is associated with the identifiedperson 1802. The tracking system 100 may add the item 1306 to thedigital cart 1410 using a process similar to the process described instep 1224 of FIG. 12.

Item Identification

FIG. 16 is a flowchart of an embodiment of an item identification method1600 for the tracking system 100. The tracking system 100 may employmethod 1600 to identify an item 1306 that has a non-uniform weight andto assign the item 1306 to a person's digital cart 1410. For items 1306with a uniform weight, the tracking system 100 is able to determine thenumber of items 1306 that are removed from a weight sensor 110 based ona weight difference on the weight sensor 110. However, items 1306 suchas fresh food do not have a uniform weight which means that the trackingsystem 100 is unable to determine how many items 1306 were removed froma shelf 1302 based on weight measurements. In this configuration, thetracking system 100 uses a sensor 108 to identify markers 1820 (e.g.text or symbols) on an item 1306 that has been picked up and to identifya person near the rack 112 where the item 1306 was picked up. Forexample, a marker 1820 may be located on the packaging of an item 1806or on a strap for carrying the item 1806. Once the item 1306 and theperson have been identified, the tracking system 100 can add the item1306 to a digital cart 1410 that is associated with the identifiedperson.

At step 1602, the tracking system 100 detects a weight decrease on aweight sensor 110. Returning to the example in FIG. 18, the weightsensor 110 is disposed on a rack 112 and is configured to measure aweight for the items 1306 that are placed on the weight sensor 110. Inthis example, the weight sensor 110 is associated with a particular item1306. The tracking system 100 detects a weight decrease on the weightsensor 110 when a person 1802 removes one or more items 1306 from theweight sensor 110.

After the tracking system 100 detects that an item 1306 was removed froma rack 112, the tracking system 100 will use a sensor 108 to identifythe item 1306 that was removed and the person who picked up the item1306. Returning to FIG. 16 at step 1604, the tracking system 100receives a frame 302 from a sensor 108. The sensor 108 captures a frame302 of at least a portion of the rack 112 within the global plane 104for the space 102. In the example shown in FIG. 18, the sensor 108 isconfigured such that the frame 302 from the sensor 108 captures anoverhead view of the rack 112. The frame 302 comprises a plurality ofpixels that are each associated with a pixel location 402. Each pixellocation 402 comprises a pixel row and a pixel column. The pixel row andthe pixel column indicate the location of a pixel within the frame 302.

The frame 302 comprises a predefined zone 1808 that is configuredsimilar to the predefined zone 1808 described in step 1504 of FIG. 15.In one embodiment, the frame 1806 may further comprise a secondpredefined zone that is configured as a virtual curtain similar to thepredefined zone 1406 that is described in FIGS. 12-14. For example, thetracking system 100 may use the second predefined zone to detect thatthe person's 1802 hand reaches for an item 1306 before detecting theweight decrease on the weight sensor 110. In this example, the secondpredefined zone is used to alert the tracking system 100 that an item1306 is about to be picked up from the rack 112 which may be used totrigger the sensor 108 to capture a frame 302 that includes the item1306 being removed from the rack 112.

At step 1606, the tracking system 100 identifies a marker 1820 on anitem 1306 within a predefined zone 1808 in the frame 302. A marker 1820is an object with unique features that can be detected by a sensor 108.For instance, a marker 1820 may comprise a uniquely identifiable shape,color, symbol, pattern, text, a barcode, a QR code, or any othersuitable type of feature. The tracking system 100 may search the frame302 for known features that correspond with a marker 1820. Referring tothe example in FIG. 18, the tracking system 100 may identify a shape(e.g. a star) on the packaging of the item 1806 in the frame 302 thatcorresponds with a marker 1820. As another example, the tracking system100 may use character or text recognition to identify alphanumeric textthat corresponds with a marker 1820 when the marker 1820 comprises text.In other examples, the tracking system 100 may use any other suitabletechnique to identify a marker 1820 within the frame 302.

Returning to FIG. 16 at step 1608, the tracking system 100 identifies anitem 1306 associated with the marker 1820. In one embodiment, thetracking system 100 comprises an item map 1308B that associates items1306 with particular markers 1820. For example, an item map 1308B maycomprise a plurality of item identifiers that are each mapped to aparticular marker 1820 (i.e. marker identifier). The tracking system 100identifies the item 1306 or item identifier that corresponds with themarker 1820 using the item map 1308B.

In some embodiments, the tracking system 100 may also use informationfrom a weight sensor 110 to identify the item 1306. For example, thetracking system 100 may comprise an item map 1308A that associates items1306 with particular locations (e.g. zone 1304 and/or shelves 1302) andweight sensors 110 on the rack 112. For example, an item map 1308A maycomprise a rack identifier, weight sensor identifiers, and a pluralityof item identifiers. Each item identifier is mapped to a particularweight sensor 110 (i.e. weight sensor identifier) on the rack 112. Thetracking system 100 determines which weight sensor 110 detected a weightdecrease and then identifies the item 1306 or item identifier thatcorresponds with the weight sensor 110 using the item map 1308A.

After the tracking system 100 identifies the item 1306 that was pickedup from the rack 112, the tracking system 100 then determines whichperson picked up the item 1306 from the rack 112. At step 1610, thetracking system 100 identifies a person 1802 within the frame 302. Thetracking system 100 may identify a person 1802 within the frame 302using a process similar to the process described in step 1004 of FIG.10. In other examples, the tracking system 100 may employ any othersuitable technique for identifying a person 1802 within the frame 302.

At step 1612, the tracking system 100 determines a pixel location 402Afor the identified person 1802. The tracking system 100 may determine apixel location 402A for the identified person 1802 using a processsimilar to the process described in step 1004 of FIG. 10. The pixellocation 402A comprises a pixel row and a pixel column that identifiesthe location of the person 1802 in the frame 302 of the sensor 108.

At step 1613, the tracking system 100 applies a homography 118 to thepixel location 402A of the identified person 1802 to determine an (x,y)coordinate 306 in the global plane 104 for the identified person 1802.The tracking system 100 may determine the (x,y) coordinate 306 in theglobal plane 104 for the identified person 1802 using a process similarto the process described in step 1511 of FIG. 15.

At step 1614, the tracking system 100 determines whether the identifiedperson 1802 is within the predefined zone 1808. Here, the trackingsystem 100 determines whether the identified person 1802 is in asuitable area for retrieving items 1306 from the rack 112. The trackingsystem 100 may determine whether the identified person 1802 is withinthe predefined zone 1808 using a process similar to the processdescribed in step 1512 of FIG. 15. The tracking system 100 proceeds tostep 1616 in response to determining that the identified person 1802 iswithin the predefined zone 1808. In this case, the tracking system 100determines the identified person 1802 is in a suitable area forretrieving items 1306 from the rack 112, for example the identifiedperson 1802 is standing in front of the rack 112. Otherwise, thetracking system 100 returns to step 1610 to identify another personwithin the frame 302. In this case, the tracking system 100 determinesthe identified person 1802 is not in a suitable area for retrievingitems 1306 from the rack 112, for example the identified person 1802 isstanding behind of the rack 112.

In some instances, multiple people may be near the rack 112 and thetracking system 100 may need to determine which person is interactingwith the rack 112 so that it can add a picked-up item 1306 to theappropriate person's digital cart 1410. The tracking system 100 mayidentify which person picked up the item 1306 from the rack 112 using aprocess similar to the process described in step 1512 of FIG. 15.

At step 1614, the tracking system 100 adds the item 1306 to a digitalcart 1410 that is associated with the person 1802. The tracking system100 may add the item 1306 to the digital cart 1410 using a processsimilar to the process described in step 1224 of FIG. 12.

Misplaced Item Identification

FIG. 17 is a flowchart of an embodiment of a misplaced itemidentification method 1700 for the tracking system 100. The trackingsystem 100 may employ method 1700 to identify items 1306 that have beenmisplaced on a rack 112. While a person is shopping, the shopper maydecide to put down one or more items 1306 that they have previouslypicked up. In this case, the tracking system 100 should identify whichitems 1306 were put back on a rack 112 and which shopper put the items1306 back so that the tracking system 100 can remove the items 1306 fromtheir digital cart 1410. Identifying an item 1306 that was put back on arack 112 is challenging because the shopper may not put the item 1306back in its correct location. For example, the shopper may put back anitem 1306 in the wrong location on the rack 112 or on the wrong rack112. In either of these cases, the tracking system 100 has to correctlyidentify both the person and the item 1306 so that the shopper is notcharged for item 1306 when they leave the space 102. In thisconfiguration, the tracking system 100 uses a weight sensor 110 to firstdetermine that an item 1306 was not put back in its correct location.The tracking system 100 then uses a sensor 108 to identify the personthat put the item 1306 on the rack 112 and analyzes their digital cart1410 to determine which item 1306 they most likely put back based on theweights of the items 1306 in their digital cart 1410.

At step 1702, the tracking system 100 detects a weight increase on aweight sensor 110. Returning to the example in FIG. 18, a first person1802 places one or more items 1306 back on a weight sensor 110 on therack 112. The weight sensor 110 is configured to measure a weight forthe items 1306 that are placed on the weight sensor 110. The trackingsystem 100 detects a weight increase on the weight sensor 110 when aperson 1802 adds one or more items 1306 to the weight sensor 110.

At step 1704, the tracking system 100 determines a weight increaseamount on the weight sensor 110 in response to detecting the weightincrease on the weight sensor 110. The weight increase amountcorresponds with a magnitude of the weight change detected by the weightsensor 110. Here, the tracking system 100 determines how much of aweight increase was experienced by the weight sensor 110 after one ormore items 1306 were placed on the weight sensor 110.

In one embodiment, the tracking system 100 determines that the item 1306placed on the weight sensor 110 is a misplaced item 1306 based on theweight increase amount. For example, the weight sensor 110 may beassociated with an item 1306 that has a known individual item weight.This means that the weight sensor 110 is only expected to experienceweight changes that are multiples of the known item weight. In thisconfiguration, the tracking system 100 may determine that the returneditem 1306 is a misplaced item 1306 when the weight increase amount doesnot match the individual item weight or multiples of the individual itemweight for the item 1306 associated with the weight sensor 110. As anexample, the weight sensor 110 may be associated with an item 1306 thathas an individual weight of ten ounces. If the weight sensor 110 detectsa weight increase of twenty-five ounces, the tracking system 100 candetermine that the item 1306 placed weight sensor 114 is not an item1306 that is associated with the weight sensor 110 because the weightincrease amount does not match the individual item weight or multiplesof the individual item weight for the item 1306 that is associated withthe weight sensor 110.

After the tracking system 100 detects that an item 1306 has been placedback on the rack 112, the tracking system 100 will use a sensor 108 toidentify the person that put the item 1306 back on the rack 112. At step1706, the tracking system 100 receives a frame 302 from a sensor 108.The sensor 108 captures a frame 302 of at least a portion of the rack112 within the global plane 104 for the space 102. In the example shownin FIG. 18, the sensor 108 is configured such that the frame 302 fromthe sensor 108 captures an overhead view of the rack 112. The frame 302comprises a plurality of pixels that are each associated with a pixellocation 402. Each pixel location 402 comprises a pixel row and a pixelcolumn. The pixel row and the pixel column indicate the location of apixel within the frame 302. In some embodiments, the frame 302 furthercomprises a predefined zone 1808 that is configured similar to thepredefined zone 1808 described in step 1504 of FIG. 15.

At step 1708, the tracking system 100 identifies a person 1802 withinthe frame 302. The tracking system 100 may identify a person 1802 withinthe frame 302 using a process similar to the process described in step1004 of FIG. 10. In other examples, the tracking system 100 may employany other suitable technique for identifying a person 1802 within theframe 302.

At step 1710, the tracking system 100 determines a pixel location 402Ain the frame 302 for the identified person 1802. The tracking system 100may determine a pixel location 402A for the identified person 1802 usinga process similar to the process described in step 1004 of FIG. 10. Thepixel location 402A comprises a pixel row and a pixel column thatidentifies the location of the person 1802 in the frame 302 of thesensor 108.

At step 1712, the tracking system 100 determines whether the identifiedperson 1802 is within a predefined zone 1808 of the frame 302. Here, thetracking system 100 determines whether the identified person 1802 is ina suitable area for putting items 1306 back on the rack 112. Thetracking system 100 may determine whether the identified person 1802 iswithin the predefined zone 1808 using a process similar to the processdescribed in step 1512 of FIG. 15. The tracking system 100 proceeds tostep 1714 in response to determining that the identified person 1802 iswithin the predefined zone 1808. In this case, the tracking system 100determines the identified person 1802 is in a suitable area for puttingitems 1306 back on the rack 112, for example the identified person 1802is standing in front of the rack 112. Otherwise, the tracking system 100returns to step 1708 to identify another person within the frame 302. Inthis case, the tracking system 100 determines the identified person isnot in a suitable area for retrieving items 1306 from the rack 112, forexample the person is standing behind of the rack 112.

In some instances, multiple people may be near the rack 112 and thetracking system 100 may need to determine which person is interactingwith the rack 112 so that it can remove the returned item 1306 from theappropriate person's digital cart 1410. The tracking system 100 maydetermine which person put back the item 1306 on the rack 112 using aprocess similar to the process described in step 1512 of FIG. 15.

After the tracking system 100 identifies which person put back the item1306 on the rack 112, the tracking system 100 then determines which item1306 from the identified person's digital cart 1410 has a weight thatclosest matches the item 1306 that was put back on the rack 112. At step1714, the tracking system 100 identifies a plurality of items 1306 in adigital cart 1410 that is associated with the person 1802. Here, thetracking system 100 identifies the digital cart 1410 that is associatedwith the identified person 1802. For example, the digital cart 1410 maybe linked with the identified person's 1802 object identifier 1118. Inone embodiment, the digital cart 1410 comprises item identifiers thatare each associated with an individual item weight. At step 1716, thetracking system 100 identifies an item weight for each of the items 1306in the digital cart 1410. In one embodiment, the tracking system 100 maycomprises a set of item weights stored in memory and may look up theitem weight for each item 1306 using the item identifiers that areassociated with the item's 1306 in the digital cart 1410.

At step 1718, the tracking system 100 identifies an item 1306 from thedigital cart 1410 with an item weight that closest matches the weightincrease amount. For example, the tracking system 100 may compare theweight increase amount measured by the weight sensor 110 to the itemweights associated with each of the items 1306 in the digital cart 1410.The tracking system 100 may then identify which item 1306 correspondswith an item weight that closest matches the weight increase amount.

In some cases, the tracking system 100 is unable to identify an item1306 in the identified person's digital cart 1410 that a weight thatmatches the measured weight increase amount on the weight sensor 110. Inthis case, the tracking system 100 may determine a probability that anitem 1306 was put down for each of the items 1306 in the digital cart1410. The probability may be based on the individual item weight and theweight increase amount. For example, an item 1306 with an individualweight that is closer to the weight increase amount will be associatedwith a higher probability than an item 1306 with an individual weightthat is further away from the weight increase amount.

In some instances, the probabilities are a function of the distancebetween a person and the rack 112. In this case, the probabilitiesassociated with items 1306 in a person's digital cart 1410 depend on howclose the person is to the rack 112 where the item 1306 was put back.For example, the probabilities associated with the items 1306 in thedigital cart 1410 may be inversely proportional to the distance betweenthe person and the rack 112. In other words, the probabilitiesassociated with the items in a person's digital cart 1410 decay as theperson moves further away from the rack 112. The tracking system 100 mayidentify the item 1306 that has the highest probability of being theitem 1306 that was put down.

In some cases, the tracking system 100 may consider items 1306 that arein multiple people's digital carts 1410 when there are multiple peoplewithin the predefined zone 1808 that is associated with the rack 112.For example, the tracking system 100 may determine a second person iswithin the predefined zone 1808 that is associated with the rack 112. Inthis example, the tracking system 100 identifies items 1306 from eachperson's digital cart 1410 that may correspond with the item 1306 thatwas put back on the rack 112 and selects the item 1306 with an itemweight that closest matches the item 1306 that was put back on the rack112. For instance, the tracking system 100 identifies item weights foritems 1306 in a second digital cart 1410 that is associated with thesecond person. The tracking system 100 identifies an item 1306 from thesecond digital cart 1410 with an item weight that closest matches theweight increase amount. The tracking system 100 determines a firstweight difference between a first identified item 1306 from digital cart1410 of the first person 1802 and the weight increase amount and asecond weight difference between a second identified item 1306 from thesecond digital cart 1410 of the second person. In this example, thetracking system 100 may determine that the first weight difference isless than the second weight difference, which indicates that the item1306 identified in the first person's digital cart 1410 closest matchesthe weight increase amount, and then removes the first identified item1306 from their digital cart 1410.

After the tracking system 100 identifies the item 1306 that most likelyput back on the rack 112 and the person that put the item 1306 back, thetracking system 100 removes the item 1306 from their digital cart 1410.At step 1720, the tracking system 100 removes the identified item 1306from the identified person's digital cart 1410. Here, the trackingsystem 100 discards information associated with the identified item 1306from the digital cart 1410. This process ensures that the shopper willnot be charged for item 1306 that they put back on a rack 112 regardlessof whether they put the item 1306 back in its correct location.

Auto-Exclusion Zones

In order to track the movement of people in the space 102, the trackingsystem 100 should generally be able to distinguish between the people(i.e., the target objects) and other objects (i.e., non-target objects),such as the racks 112, displays, and any other non-human objects in thespace 102. Otherwise, the tracking system 100 may waste memory andprocessing resources detecting and attempting to track these non-targetobjects. As described elsewhere in this disclosure (e.g., in FIGS. 24-26and corresponding description below), in some cases, people may betracked may be performed by detecting one or more contours in a set ofimage frames (e.g., a video) and monitoring movements of the contourbetween frames. A contour is generally a curve associated with an edgeof a representation of a person in an image. While the tracking system100 may detect contours in order to track people, in some instances, itmay be difficult to distinguish between contours that correspond topeople (e.g., or other target objects) and contours associated withnon-target objects, such as racks 112, signs, product displays, and thelike.

Even if sensors 108 are calibrated at installation to account for thepresence of non-target objects, in many cases, it may be challenging toreliably and efficiently recalibrate the sensors 108 to account forchanges in positions of non-target objects that should not be tracked inthe space 102. For example, if a rack 112, sign, product display, orother furniture or object in space 102 is added, removed, or moved(e.g., all activities which may occur frequently and which may occurwithout warning and/or unintentionally), one or more of the sensors 108may require recalibration or adjustment. Without this recalibration oradjustment, it is difficult or impossible to reliably track people inthe space 102. Prior to this disclosure, there was a lack of tools forefficiently recalibrating and/or adjusting sensors, such as sensors 108,in a manner that would provide reliable tracking.

This disclosure encompasses the recognition not only of the previouslyunrecognized problems described above (e.g., with respect to trackingpeople in space 102, which may change over time) but also providesunique solutions to these problems. As described in this disclosure,during an initial time period before people are tracked, pixel regionsfrom each sensor 108 may be determined that should be excluded duringsubsequent tracking. For example, during the initial time period, thespace 102 may not include any people such that contours detected by eachsensor 108 correspond only to non-target objects in the space for whichtracking is not desired. Thus, pixel regions, or “auto-exclusion zones,”corresponding to portions of each image generated by sensors 108 thatare not used for object detection and tracking (e.g., the pixelcoordinates of contours that should not be tracked). For instance, theauto-exclusion zones may correspond to contours detected in images thatare associated with non-target objects, contours that are spuriouslydetected at the edges of a sensor's field-of-view, and the like).Auto-exclusion zones can be determined automatically at any desired orappropriate time interval to improve the usability and performance oftracking system 100.

After the auto-exclusion zones are determined, the tracking system 100may proceed to track people in the space 102. The auto-exclusion zonesare used to limit the pixel regions used by each sensor 108 for trackingpeople. For example, pixels corresponding to auto-exclusion zones may beignored by the tracking system 100 during tracking. In some cases, adetected person (e.g., or other target object) may be near or partiallyoverlapping with one or more auto-exclusion zones. In these cases, thetracking system 100 may determine, based on the extent to which apotential target object's position overlaps with the auto-exclusionzone, whether the target object will be tracked. This may reduce oreliminate false positive detection of non-target objects during persontracking in the space 102, while also improving the efficiency oftracking system 100 by reducing wasted processing resources that wouldotherwise be expended attempting to track non-target objects. In someembodiments, a map of the space 102 may be generated that presents thephysical regions that are excluded during tracking (i.e., a map thatpresents a representation of the auto-exclusion zone(s) in the physicalcoordinates of the space). Such a map, for example, may facilitatetrouble-shooting of the tracking system by allowing an administrator tovisually confirm that people can be tracked in appropriate portions ofthe space 102.

FIG. 19 illustrates the determination of auto-exclusion zones 1910, 1914and the subsequent use of these auto-exclusion zones 1910, 1914 forimproved tracking of people (e.g., or other target objects) in the space102. In general, during an initial time period (t<t₀), top-view imageframes are received by the client(s) 105 and/or server 106 from sensors108 and used to determine auto-exclusion zones 1910, 1914. For instance,the initial time period at t<t₀ may correspond to a time when no peopleare in the space 102. For example, if the space 102 is open to thepublic during a portion of the day, the initial time period may bebefore the space 102 is opened to the public. In some embodiments, theserver 106 and/or client 105 may provide, for example, an alert ortransmit a signal indicating that the space 102 should be emptied ofpeople (e.g., or other target objects to be tracked) in order forauto-exclusion zones 1910, 1914 to be identified. In some embodiments, auser may input a command (e.g., via any appropriate interface coupled tothe server 106 and/or client(s) 105) to initiate the determination ofauto-exclusion zones 1910, 1914 immediately or at one or more desiredtimes in the future (e.g., based on a schedule).

An example top-view image frame 1902 used for determining auto-exclusionzones 1910, 1914 is shown in FIG. 19. Image frame 1902 includes arepresentation of a first object 1904 (e.g., a rack 112) and arepresentation of a second object 1906. For instance, the first object1904 may be a rack 112, and the second object 1906 may be a productdisplay or any other non-target object in the space 102. In someembodiments, the second object 1906 may not correspond to an actualobject in the space but may instead be detected anomalously because oflighting in the space 102 and/or a sensor error. Each sensor 108generally generates at least one frame 1902 during the initial timeperiod, and these frame(s) 1902 is/are used to determine correspondingauto-exclusion zones 1910, 1914 for the sensor 108. For instance, thesensor client 105 may receive the top-view image 1902, and detectcontours (i.e., the dashed lines around zones 1910, 1914) correspondingto the auto-exclusion zones 1910, 1914 as illustrated in view 1908. Thecontours of auto-exclusion zones 1910, 1914 generally correspond tocurves that extend along a boundary (e.g., the edge) of objects 1904,1906 in image 1902. The view 1908 generally corresponds to apresentation of image 1902 in which the detected contours correspondingto auto-exclusion zones 1910, 1914 are presented but the correspondingobjects 1904, 1906, respectively, are not shown. For an image frame 1902that includes color and depth data, contours for auto-exclusion zones1910, 1914 may be determined at a given depth (e.g., a distance awayfrom sensor 108) based on the color data in the image 1902. For example,a steep gradient of a color value may correspond to an edge of an objectand used to determine, or detect, a contour. For example, contours forthe auto-exclusion zones 1910, 1914 may be determined using any suitablecontour or edge detection method such as Canny edge detection,threshold-based detection, or the like.

The client 105 determines pixel coordinates 1912 and 1916 correspondingto the locations of the auto-exclusions zones 1910 and 1914,respectively. The pixel coordinates 1912, 1916 generally correspond tothe locations (e.g., row and column numbers) in the image frame 1902that should be excluded during tracking. In general, objects associatedwith the pixel coordinates 1912, 1916 are not tracked by the trackingsystem 100. Moreover, certain objects which are detected outside of theauto-exclusion zones 1910, 1914 may not be tracked under certainconditions. For instance, if the position of the object (e.g., theposition associated with region 1920, discussed below with respect toview 1914) overlaps at least a threshold amount with an auto-exclusionzone 1910, 1914, the object may not be tracked. This prevents thetracking system 100 (i.e., or the local client 105 associated with asensor 108 or a subset of sensors 108) from attempting to unnecessarilytrack non-target objects. In some cases, auto-exclusion zones 1910, 1914correspond to non-target (e.g., inanimate) objects in the field-of-viewof a sensor 108 (e.g., a rack 112, which is associated with contour1910). However, auto-exclusion zones 1910, 1914 may also oralternatively correspond to other aberrant features or contours detectedby a sensor 108 (e.g., caused by sensor errors, inconsistent lighting,or the like).

Following the determination of pixel coordinates 1912, 1916 to excludeduring tracking, objects may be tracked during a subsequent time periodcorresponding to t>t₀. An example image frame 1918 generated duringtracking is shown in FIG. 19. In frame 1918, region 1920 is detected aspossibly corresponding to what may or may not be a target object. Forexample, region 1920 may correspond to a pixel mask or bounding boxgenerated based on a contour detected in frame 1902. For example, apixel mask may be generated to fill in the area inside the contour or abounding box may be generated to encompass the contour. For example, apixel mask may include the pixel coordinates within the correspondingcontour. For instance, the pixel coordinates 1912 of auto-exclusion zone1910 may effectively correspond to a mask that overlays or “fills in”the auto-exclusion zone 1910. Following the detection of region 1920,the client 105 determines whether the region 1920 corresponds to atarget object which should tracked or is sufficiently overlapping withauto-exclusion zone 1914 to consider region 1920 as being associatedwith a non-target object. For example, the client 105 may determinewhether at least a threshold percentage of the pixel coordinates 1916overlap with (e.g., are the same as) pixel coordinates of region 1920.The overlapping region 1922 of these pixel coordinates is illustrated inframe 1918. For example, the threshold percentage may be about 50% ormore. In some embodiments, the threshold percentage may be as small asabout 10%. In response to determining that at least the thresholdpercentage of pixel coordinates overlap, the client 105 generally doesnot determine a pixel position for tracking the object associated withregion 1920. However, if overlap 1922 correspond to less than thethreshold percentage, an object associated with region 1920 is tracked,as described further below (e.g., with respect to FIGS. 24-26).

As described above, sensors 108 may be arranged such that adjacentsensors 108 have overlapping fields-of-view. For instance,fields-of-view of adjacent sensors 108 may overlap by between about 10%to 30%. As such, the same object may be detected by two differentsensors 108 and either included or excluded from tracking in the imageframes received from each sensor 108 based on the unique auto-exclusionzones determined for each sensor 108. This may facilitate more reliabletracking than was previously possible, even when one sensor 108 may havea large auto-exclusion zone (i.e., where a large proportion of pixelcoordinates in image frames generated by the sensor 108 are excludedfrom tracking). Accordingly, if one sensor 108 malfunctions, adjacentsensors 108 may still provide adequate tracking in the space 102.

If region 1920 corresponds to a target object (i.e., a person to trackin the space 102), the tracking system 100 proceeds to track the region1920. Example methods of tracking are described in greater detail belowwith respect to FIGS. 24-26. In some embodiments, the server 106 usesthe pixel coordinates 1912, 1916 to determine corresponding physicalcoordinates (e.g., coordinates 2012, 2016 illustrated in FIG. 20,described below). For instance, the client 105 may determine pixelcoordinates 1912, 1916 corresponding to the local auto-exclusion zones1910, 1914 of a sensor 108 and transmit these coordinates 1912, 1916 tothe server 106. As shown in FIG. 20, the server 106 may use the pixelcoordinates 1912, 1916 received from the sensor 108 to determinecorresponding physical coordinates 2010, 2016. For instance, ahomography generated for each sensor 108 (see FIGS. 2-7 and thecorresponding description above), which associates pixel coordinates(e.g., coordinates 1912, 1916) in an image generated by a given sensor108 to corresponding physical coordinates (e.g., coordinates 2012, 2016)in the space 102, may be employed to convert the excluded pixelcoordinates 1912, 1916 (of FIG. 19) to excluded physical coordinates2012, 2016 in the space 102. These excluded coordinates 2010, 2016 maybe used along with other coordinates from other sensors 108 to generatethe global auto-exclusion zone map 2000 of the space 102 which isillustrated in FIG. 20. This map 2000, for example, may facilitatetrouble-shooting of the tracking system 100 by facilitatingquantification, identification, and/or verification of physical regions2002 of space 102 where objects may (and may not) be tracked. This mayallow an administrator or other individual to visually confirm thatobjects can be tracked in appropriate portions of the space 102). Ifregions 2002 correspond to known high-traffic zones of the space 102,system maintenance may be appropriate (e.g., which may involvereplacing, adjusting, and/or adding additional sensors 108).

FIG. 21 is a flowchart illustrating an example method 2100 forgenerating and using auto-exclusion zones (e.g., zones 1910, 1914 ofFIG. 19). Method 2100 may begin at step 2102 where one or more imageframes 1902 are received during an initial time period. As describedabove, the initial time period may correspond to an interval of timewhen no person is moving throughout the space 102, or when no person iswithin the field-of-view of one or more sensors 108 from which the imageframe(s) 1902 is/are received. In a typical embodiment, one or moreimage frames 1902 are generally received from each sensor 108 of thetracking system 100, such that local regions (e.g., auto-exclusion zones1910, 1914) to exclude for each sensor 108 may be determined. In someembodiments, a single image frame 1902 is received from each sensor 108to detect auto-exclusion zones 1910, 1914. However, in otherembodiments, multiple image frames 1902 are received from each sensor108. Using multiple image frames 1902 to identify auto-exclusions zones1910, 1914 for each sensor 108 may improve the detection of any spuriouscontours or other aberrations that correspond to pixel coordinates(e.g., coordinates 1912, 1916 of FIG. 19) which should be ignored orexcluded during tracking.

At step 2104, contours (e.g., dashed contour lines corresponding toauto-exclusion zones 1910, 1914 of FIG. 19) are detected in the one ormore image frames 1902 received at step 2102. Any appropriate contourdetection algorithm may be used including but not limited to those basedon Canny edge detection, threshold-based detection, and the like. Insome embodiments, the unique contour detection approaches described inthis disclosure may be used (e.g., to distinguish closely spacedcontours in the field-of-view, as described below, for example, withrespect to FIGS. 22 and 23). At step 2106, pixel coordinates (e.g.,coordinates 1912, 1916 of FIG. 19) are determined for the detectedcontours (from step 2104). The coordinates may be determined, forexample, based on a pixel mask that overlays the detected contours. Apixel mask may for example, correspond to pixels within the contours. Insome embodiments, pixel coordinates correspond to the pixel coordinateswithin a bounding box determined for the contour (e.g., as illustratedin FIG. 22, described below). For instance, the bounding box may be arectangular box with an area that encompasses the detected contour. Atstep 2108, the pixel coordinates are stored. For instance, the client105 may store the pixel coordinates corresponding to auto-exclusionzones 1910, 1914 in memory (e.g., memory 3804 of FIG. 38, describedbelow). As described above, the pixel coordinates may also oralternatively be transmitted to the server 106 (e.g., to generate a map2000 of the space, as illustrated in the example of FIG. 20).

At step 2110, the client 105 receives an image frame 1918 during asubsequent time during which tracking is performed (i.e., after thepixel coordinates corresponding to auto-exclusion zones are stored atstep 2108). The frame is received from sensor 108 and includes arepresentation of an object in the space 102. At step 2112, a contour isdetected in the frame received at step 2110. For example, the contourmay correspond to a curve along the edge of object represented in theframe 1902. The pixel coordinates determined at step 2106 may beexcluded (or not used) during contour detection. For instance, imagedata may be ignored and/or removed (e.g., given a value of zero, or thecolor equivalent) at the pixel coordinates determined at step 2106, suchthat no contours are detected at these coordinates. In some cases, acontour may be detected outside of these coordinates. In some cases, acontour may be detected that is partially outside of these coordinatesbut overlaps partially with the coordinates (e.g., as illustrated inimage 1918 of FIG. 19).

At step 2114, the client 105 generally determines whether the detectedcontour has a pixel position that sufficiently overlaps with pixelcoordinates of the auto-exclusion zones 1910, 1914 determined at step2106. If the coordinates sufficiently overlap, the contour or region1920 (i.e., and the associated object) is not tracked in the frame. Forinstance, as described above, the client 105 may determine whether thedetected contour or region 1920 overlaps at least a threshold percentage(e.g., of 50%) with a region associated with the pixel coordinates(e.g., see overlapping region 1922 of FIG. 19). If the criteria of step2114 are satisfied, the client 105 generally, at step 2116, does notdetermine a pixel position for the contour detected at step 2112. Assuch, no pixel position is reported to the server 106, thereby reducingor eliminating the waste of processing resources associated withattempting to track an object when it is not a target object for whichtracking is desired.

Otherwise, if the criteria of step 2114 are satisfied, the client 105determines a pixel position for the contour or region 1920 at step 2118.Determining a pixel position from a contour may involve, for example,(i) determining a region 1920 (e.g., a pixel mask or bounding box)associated with the contour and (ii) determining a centroid or othercharacteristic position of the region as the pixel position. At step2120, the determined pixel position is transmitted to the server 106 tofacilitate global tracking, for example, using predeterminedhomographies, as described elsewhere in this disclosure (e.g., withrespect to FIGS. 24-26). For example, the server 106 may receive thedetermined pixel position, access a homography associating pixelcoordinates in images generated by the sensor 108 from which the frameat step 2110 was received to physical coordinates in the space 102, andapply the homography to the pixel coordinates to generate correspondingphysical coordinates for the tracked object associated with the contourdetected at step 2112.

Modifications, additions, or omissions may be made to method 2100depicted in FIG. 21. Method 2100 may include more, fewer, or othersteps. For example, steps may be performed in parallel or in anysuitable order. While at times discussed as tracking system 100,client(s) 105, server 106, or components of any of thereof performingsteps, any suitable system or components of the system may perform oneor more steps of the method.

Contour-Based Detection of Closely Spaced People

In some cases, two people are near each other, making it difficult orimpossible to reliably detect and/or track each person (e.g., or othertarget object) using conventional tools. In some cases, the people maybe initially detected and tracked using depth images at an approximatewaist depth (i.e., a depth corresponding to the waist height of anaverage person being tracked). Tracking at an approximate waist depthmay be more effective at capturing all people regardless of their heightor mode of movement. For instance, by detecting and tacking people at anapproximate waist depth, the tracking system 100 is highly likely todetect tall and short individuals and individuals who may be usingalternative methods of movement (e.g., wheelchairs, and the like).However, if two people with a similar height are standing near eachother, it may be difficult to distinguish between the two people in thetop-view images at the approximate waist depth. Rather than detectingtwo separate people, the tracking system 100 may initially detect thepeople as a single larger object.

This disclosure encompasses the recognition that at a decreased depth(i.e., a depth nearer the heads of the people), the people may be morereadily distinguished. This is because the people's heads are morelikely to be imaged at the decreased depth, and their heads are smallerand less likely to be detected as a single merged region (or contour, asdescribed in greater detail below). As another example, if two peopleenter the space 102 standing close to one another (e.g., holding hands),they may appear to be a single larger object. Since the tracking system100 may initially detect the two people as one person, it may bedifficult to properly identify these people if these people separatewhile in the space 102. As yet another example, if two people whobriefly stand close together are momentarily “lost” or detected as onlya single, larger object, it may be difficult to correctly identify thepeople after they separate from one another.

As described elsewhere in this disclosure (e.g., with respect to FIGS.19-21 and 24-26), people (e.g., the people in the example scenariosdescribed above) may be tracked by detecting contours in top-view imageframes generated by sensors 108 and tracking the positions of thesecontours. However, when two people are closely spaced, a single mergedcontour (see merged contour 2220 of FIG. 22 described below) may bedetected in a top-view image of the people. This single contourgenerally cannot be used to track each person individually, resulting inconsiderable downstream errors during tracking. For example, even if twopeople separate after having been closely spaced, it may be difficult orimpossible using previous tools to determine which person was which, andthe identity of each person may be unknown after the two peopleseparate. Prior to this disclosure, there was a lack of reliable toolsfor detecting people (e.g., and other target objects) under the examplescenarios described above and under other similar circumstances.

The systems and methods described in this disclosure provideimprovements to previous technology by facilitating the improveddetection of closely spaced people. For example, the systems and methodsdescribed in this disclosure may facilitate the detection of individualpeople when contours associated with these people would otherwise bemerged, resulting in the detection of a single person using conventionaldetection strategies. In some embodiments, improved contour detection isachieved by detecting contours at different depths (e.g., at least twodepths) to identify separate contours at a second depth within a largermerged contour detected at a first depth used for tracking. For example,if two people are standing near each other such that contours are mergedto form a single contour, separate contours associated with heads of thetwo closely spaced people may be detected at a depth associated with thepersons' heads. In some embodiments, a unique statistical approach maybe used to differentiate between the two people by selecting boundingregions for the detected contours with a low similarity value. In someembodiments, certain criteria are satisfied to ensure that the detectedcontours correspond to separate people, thereby providing more reliableperson (e.g., or other target object) detection than was previouslypossible. For example, two contours detected at an approximate headdepth may be required to be within a threshold size range in order forthe contours to be used for subsequent tracking. In some embodiments, anartificial neural network may be employed to detect separate people thatare closely spaced by analyzing top-view images at different depths.

FIG. 22 is a diagram illustrating the detection of two closely spacedpeople 2202, 2204 based on top-view depth images 2212 and angled-viewimages 2214 received from sensors 108 a,b using the tracking system 100.In one embodiment, sensors 108 a,b may each be one of sensors 108 oftracking system 100 described above with respect to FIG. 1. In anotherembodiment, sensors 108 a,b may each be one of sensors 108 of a separatevirtual store system (e.g, layout cameras and/or rack cameras) asdescribed in U.S. patent application Ser. No. ______ entitled,“Customer-Based Video Feed” (attorney docket no. 090278.0187) which isincorporated by reference herein. In this embodiment, the sensors 108 oftracking system 100 may be mapped to the sensors 108 of the virtualstore system using a homography. Moreover, this embodiment can retrieveidentifiers and the relative position of each person from the sensors108 of the virtual store system using the homography between trackingsystem 100 and the virtual store system. Generally, sensor 108 a is anoverhead sensor configured to generate top-view depth images 2212 (e.g.,color and/or depth images) of at least a portion of the space 102.Sensor 108 a may be mounted, for example, in a ceiling of the space 102.Sensor 108 a may generate image data corresponding to a plurality ofdepths which include but are not necessarily limited to the depths 2210a-c illustrated in FIG. 22. Depths 2210 a-c are generally distancesmeasured from the sensor 108 a. Each depth 2210 a-c may be associatedwith a corresponding height (e.g., from the floor of the space 102 inwhich people 2202, 2204 are detected and/or tracked). Sensor 108 aobserves a field-of-view 2208 a. Top-view images 2212 generated bysensor 108 a may be transmitted to the sensor client 105 a. The sensorclient 105 a is communicatively coupled (e.g., via wired connection ofwirelessly) to the sensor 108 a and the server 106. Server 106 isdescribed above with respect to FIG. 1.

In this example, sensor 108 b is an angled-view sensor, which isconfigured to generate angled-view images 2214 (e.g., color and/or depthimages) of at least a portion of the space 102. Sensor 108 b has a fieldof view 2208 b, which overlaps with at least a portion of thefield-of-view 2208 a of sensor 108 a. The angled-view images 2214generated by the angled-view sensor 108 b are transmitted to sensorclient 105 b. Sensor client 105 b may be a client 105 described abovewith respect to FIG. 1. In the example of FIG. 22, sensors 108 a,b arecoupled to different sensor clients 105 a,b. However, it should beunderstood that the same sensor client 105 may be used for both sensors108 a,b (e.g., such that clients 105 a,b are the same client 105). Insome cases, the use of different sensor clients 105 a,b for sensors 108a,b may provide improved performance because image data may still beobtained for the area shared by fields-of-view 2208 a,b even if one ofthe clients 105 a,b were to fail.

In the example scenario illustrated in FIG. 22, people 2202, 2204 arelocated sufficiently close together such that conventional objectdetection tools fail to detect the individual people 2202, 2204 (e.g.,such that people 2202, 2204 would not have been detected as separateobjects). This situation may correspond, for example, to the distance2206 a between people 2202, 2204 being less than a threshold distance2206 b (e.g., of about 6 inches). The threshold distance 2206 b cangenerally be any appropriate distance determined for the system 100. Forexample, the threshold distance 2206 b may be determined based onseveral characteristics of the system 2200 and the people 2202, 2204being detected. For example, the threshold distance 2206 b may be basedon one or more of the distance of the sensor 108 a from the people 2202,2204, the size of the people 2202, 2204, the size of the field-of-view2208 a, the sensitivity of the sensor 108 a, and the like. Accordingly,the threshold distance 2206 b may range from just over zero inches toover six inches depending on these and other characteristics of thetracking system 100. People 2202, 2204 may be any target object anindividual may desire to detect and/or track based on data (i.e.,top-view images 2212 and/or angled-view images 2214) from sensors 108a,b.

The sensor client 105 a detects contours in top-view images 2212received from sensor 108 a. Typically, the sensor client 105 a detectscontours at an initial depth 2210 a. The initial depth 2210 a may beassociated with, for example, a predetermined height (e.g., from theground) which has been established to detect and/or track people 2202,2204 through the space 102. For example, for tracking humans, theinitial depth 2210 a may be associated with an average shoulder or waistheight of people expected to be moving in the space 102 (e.g., a depthwhich is likely to capture a representation for both tall and shortpeople traversing the space 102). The sensor client 105 a may use thetop-view images 2212 generated by sensor 108 a to identify the top-viewimage 2212 corresponding to when a first contour 2202 a associated withthe first person 2202 merges with a second contour 2204 a associatedwith the second person 2204. View 2216 illustrates contours 2202 a, 2204a at a time prior to when these contours 2202 a, 2204 a merge (i.e.,prior to a time (t_(close)) when the first and second people 2202, 2204are within the threshold distance 2206 b of each other). View 2216corresponds to a view of the contours detected in a top-view image 2212received from sensor 108 a (e.g., with other objects in the image notshown).

A subsequent view 2218 corresponds to the image 2212 at or neart_(close) when the people 2202, 2204 are closely spaced and the firstand second contours 2202 a, 2204 a merge to form merged contour 2220.The sensor client 105 a may determine a region 2222 which corresponds toa “size” of the merged contour 2220 in image coordinates (e.g., a numberof pixels associated with contour 2220). For example, region 2222 maycorrespond to a pixel mask or a bounding box determined for contour2220. Example approaches to determining pixel masks and bounding boxesare described above with respect to step 2104 of FIG. 21. For example,region 2222 may be a bounding box determined for the contour 2220 usinga non-maximum suppression object-detection algorithm. For instance, thesensor client 105 a may determine a plurality of bounding boxesassociated with the contour 2220. For each bounding box, the client 105a may calculate a score. The score, for example, may represent an extentto which that bounding box is similar to the other bounding boxes. Thesensor client 105 a may identify a subset of the bounding boxes with ascore that is greater than a threshold value (e.g., 80% or more), anddetermine region 2222 based on this identified subset. For example,region 2222 may be the bounding box with the highest score or a boundingcomprising regions shared by bounding boxes with a score that is abovethe threshold value.

In order to detect the individual people 2202 and 2204, the sensorclient 105 a may access images 2212 at a decreased depth (i.e., at oneor both of depths 2212 b and 2212 c) and use this data to detectseparate contours 2202 b, 2204 b, illustrated in view 2224. In otherwords, the sensor client 105 a may analyze the images 2212 at a depthnearer the heads of people 2202, 2204 in the images 2212 in order todetect the separate people 2202, 2204. In some embodiments, thedecreased depth may correspond to an average or predetermined headheight of persons expected to be detected by the tracking system 100 inthe space 102. In some cases, contours 2202 b, 2204 b may be detected atthe decreased depth for both people 2202, 2204.

However, in other cases, the sensor client 105 a may not detect bothheads at the decreased depth. For example, if a child and an adult areclosely spaced, only the adult's head may be detected at the decreaseddepth (e.g., at depth 2210 b). In this scenario, the sensor client 105 amay proceed to a slightly increased depth (e.g., to depth 2210 c) todetect the head of the child. For instance, in such scenarios, thesensor client 105 a iteratively increases the depth from the decreaseddepth towards the initial depth 2210 a in order to detect two distinctcontours 2202 b, 2204 b (e.g., for both the adult and the child in theexample described above). For instance, the depth may first be decreasedto depth 2210 b and then increased to depth 2210 c if both contours 2202b and 2204 b are not detected at depth 2210 b. This iterative process isdescribed in greater detail below with respect to method 2300 of FIG.23.

As described elsewhere in this disclosure, in some cases, the trackingsystem 100 may maintain a record of features, or descriptors, associatedwith each tracked person (see, e.g., FIG. 30, described below). As such,the sensor client 105 a may access this record to determine uniquedepths that are associated with the people 2202, 2204, which are likelyassociated with merged contour 2220. For instance, depth 2210 b may beassociated with a known head height of person 2202, and depth 2212 c maybe associated with a known head height of person 2204.

Once contours 2202 b and 2204 b are detected, the sensor clientdetermines a region 2202 c associated with pixel coordinates 2202 d ofcontour 2202 b and a region 2204 c associated with pixel coordinates2204 d of contour 2204 b. For example, as described above with respectto region 2222, regions 2202 c and 2204 c may correspond to pixel masksor bounding boxes generated based on the corresponding contours 2202 b,2204 b, respectively. For example, pixel masks may be generated to “fillin” the area inside the contours 2202 b, 2204 b or bounding boxes may begenerated which encompass the contours 2202 b, 2204 b. The pixelcoordinates 2202 d, 2204 d generally correspond to the set of positions(e.g., rows and columns) of pixels within regions 2202 c, 2204 c.

In some embodiments, a unique approach is employed to more reliablydistinguish between closely spaced people 2202 and 2204 and determineassociated regions 2202 c and 2204 c. In these embodiments, the regions2202 c and 2204 c are determined using a unique method referred to inthis disclosure as “non-minimum suppression.” Non-minimum suppressionmay involve, for example, determining bounding boxes associated with thecontour 2202 b, 2204 b (e.g., using any appropriate object detectionalgorithm as appreciated by a person of skilled in the relevant art).For each bounding box, a score may be calculated. As described abovewith respect to non-maximum suppression, the score may represent anextent to which the bounding box is similar to the other bounding boxes.However, rather than identifying bounding boxes with high scores (e.g.,as with non-maximum suppression), a subset of the bounding boxes isidentified with scores that are less than a threshold value (e.g., ofabout 20%). This subset may be used to determine regions 2202 c, 2204 c.For example, regions 2202 c, 2204 c may include regions shared by eachbounding box of the identified subsets. In other words, bounding boxesthat are not below the minimum score are “suppressed” and not used toidentify regions 2202 b, 2204 b.

Prior to assigning a position or identity to the contours 2202 b, 2204 band/or the associated regions 2202 c, 2204 c, the sensor client 105 amay first check whether criteria are satisfied for distinguishing theregion 2202 c from region 2204 c. The criteria are generally designed toensure that the contours 2202 b, 2204 b (and/or the associated regions2202 c, 2204 c) are appropriately sized, shaped, and positioned to beassociated with the heads of the corresponding people 2202, 2204. Thesecriteria may include one or more requirements. For example, onerequirement may be that the regions 2202 c, 2204 c overlap by less thanor equal to a threshold amount (e.g., of about 50%, e.g., of about 10%).Generally, the separate heads of different people 2202, 2204 should notoverlap in a top-view image 2212. Another requirement may be that theregions 2202 c, 2204 c are within (e.g., bounded by, e.g., encompassedby) the merged-contour region 2222. This requirement, for example,ensures that the head contours 2202 b, 2204 b are appropriatelypositioned above the merged contour 2220 to correspond to heads ofpeople 2202, 2204. If the contours 2202 b, 2204 b detected at thedecreased depth are not within the merged contour 2220, then thesecontours 2202 b, 2204 b are likely not the associated with heads of thepeople 2202, 2204 associated with the merged contour 2220.

Generally, if the criteria are satisfied, the sensor client 105 aassociates region 2202 c with a first pixel position 2202 e of person2202 and associates region 2204 c with a second pixel position 2204 e ofperson 2204. Each of the first and second pixel positions 2202 e, 2204 egenerally corresponds to a single pixel position (e.g., row and column)associated with the location of the corresponding contour 2202 b, 2204 bin the image 2212. The first and second pixel positions 2202 e, 2204 eare included in the pixel positions 2226 which may be transmitted to theserver 106 to determine corresponding physical (e.g., global) positions2228, for example, based on homographies 2230 (e.g., using a previouslydetermined homography for sensor 108 a associating pixel coordinates inimages 2212 generated by sensor 108 a to physical coordinates in thespace 102).

As described above, sensor 108 b is positioned and configured togenerate angled-view images 2214 of at least a portion of the fieldof-of-view 2208 a of sensor 108 a. The sensor client 105 b receives theangled-view images 2214 from the second sensor 108 b. Because of itsdifferent (e.g., angled) view of people 2202, 2204 in the space 102, anangled-view image 2214 obtained at t_(close) may be sufficient todistinguish between the people 2202, 2204. A view 2232 of contours 2202d, 2204 d detected at t_(close) is shown in FIG. 22. The sensor client105 b detects a contour 2202 f corresponding to the first person 2202and determines a corresponding region 2202 g associated with pixelcoordinates 2202 h of contour 2202 f The sensor client 105 b detects acontour 2204 f corresponding to the second person 2204 and determines acorresponding region 2204 g associated with pixel coordinates 2204 h ofcontour 2204 f. Since contours 2202 f, 2204 f do not merge and regions2202 g, 2204 g are sufficiently separated (e.g., they do not overlapand/or are at least a minimum pixel distance apart), the sensor client105 b may associate region 2202 g with a first pixel position 2202 i ofthe first person 2202 and region 2204 g with a second pixel position2204 i of the second person 2204. Each of the first and second pixelpositions 2202 i, 2204 i generally corresponds to a single pixelposition (e.g., row and column) associated with the location of thecorresponding contour 2202 f, 2204 f in the image 2214. Pixel positions2202 i, 2204 i may be included in pixel positions 2234 which may betransmitted to server 106 to determine physical positions 2228 of thepeople 2202, 2204 (e.g., using a previously determined homography forsensor 108 b associating pixel coordinates of images 2214 generated bysensor 108 b to physical coordinates in the space 102).

In an example operation of the tracking system 100 sensor 108 a isconfigured to generate top-view color-depth images of at least a portionof the space 102. When people 2202 and 2204 are within a thresholddistance of each another, the sensor client 105 a identifies an imageframe (e.g., associated with view 2218) corresponding to a time stamp(e.g., t_(close)) where contours 2202 a, 2204 a associated with thefirst and second person 2202, 2204, respectively, are merged and formcontour 2220. In order to detect each person 2202 and 2204 in theidentified image frame (e.g., associated with view 2218), the client 105a may first attempt to detect separate contours for each person 2202,2204 at a first decreased depth 2210 b. As described above, depth 2210 bmay be a predetermined height associated with an expected head height ofpeople moving through the space 102. In some embodiments, depth 2210 bmay be a depth previously determined based on a measured height ofperson 2202 and/or a measured height of person 2204. For example, depth2210 b may be based on an average height of the two people 2202, 2204.As another example, depth 2210 b may be a depth corresponding to apredetermined head height of person 2202 (as illustrated in the exampleof FIG. 22). If two contours 2202 b, 2204 b are detected at depth 2210b, these contours may be used to determine pixel positions 2202 e, 2204e of people 2202 and 2204, as described above.

If only one contour 2202 b is detected at depth 2210 b (e.g., if onlyone person 2202, 2204 is tall enough to be detected at depth 2210 b),the region associated with this contour 2202 b may be used to determinethe pixel position 2202 e of the corresponding person, and the nextperson may be detected at an increased depth 2210 c. Depth 2210 c isgenerally greater than 2210 b but less than depth 2210 a. In theillustrative example of FIG. 22, depth 2210 c corresponds to apredetermined head height of person 2204. If contour 2204 b is detectedfor person 2204 at depth 2210 c, a pixel position 2204 e is determinedbased on pixel coordinates 2204 d associated with the contour 2204 b(e.g., following determination that the criteria described above aresatisfied). If a contour 2204 b is not detected at depth 2210 c, theclient 105 a may attempt to detect contours at progressively increaseddepths until a contour is detected or a maximum depth (e.g., the initialdepth 2210 a) is reached. For example, the sensor client 105 a maycontinue to search for the contour 2204 b at increased depths (i.e.,depths between depth 2210 c and the initial depth 2210 a). If themaximum depth (e.g., depth 2210 a) is reached without the contour 2204 bbeing detected, the client 105 a generally determines that the separatepeople 2202, 2204 cannot be detected.

FIG. 23 is a flowchart illustrating a method 2300 of operating trackingsystem 100 to detect closely spaced people 2202, 2204. Method 2300 maybegin at step 2302 where the sensor client 105 a receives one or moreframes of top-view depth images 2212 generated by sensor 108 a. At step2304, the sensor client 105 a identifies a frame in which a firstcontour 2202 a associated with the first person 2202 is merged with asecond contour 2204 a associated with the second person 2204. Generally,the merged first and second contours (i.e., merged contour 2220) isdetermined at the first depth 2212 a in the depth images 2212 receivedat step 2302. The first depth 2212 a may correspond to a waist or shoulddepth of persons expected to be tracked in the space 102. The detectionof merged contour 2220 corresponds to the first person 2202 beinglocated in the space within a threshold distance 2206 b from the secondperson 2204, as described above.

At step 2306, the sensor client 105 a determines a merged-contour region2222. Region 2222 is associated with pixel coordinates of the mergedcontour 2220. For instance, region 2222 may correspond to coordinates ofa pixel mask that overlays the detected contour. As another example,region 2222 may correspond to pixel coordinates of a bounding boxdetermined for the contour (e.g., using any appropriate object detectionalgorithm). In some embodiments, a method involving non-maximumsuppression is used to detect region 2222. In some embodiments, region2222 is determined using an artificial neural network. For example, anartificial neural network may be trained to detect contours at variousdepths in top-view images generated by sensor 108 a.

At step 2308, the depth at which contours are detected in the identifiedimage frame from step 2304 is decreased (e.g., to depth 2210 billustrated in FIG. 22). At step 2310 a, the sensor client 105 adetermines whether a first contour (e.g., contour 2202 b) is detected atthe current depth. If the contour 2202 b is not detected, the sensorclient 105 a proceeds, at step 2312 a, to an increased depth (e.g., todepth 2210 c). If the increased depth corresponds to having reached amaximum depth (e.g., to reaching the initial depth 2210 a), the processends because the first contour 2202 b was not detected. If the maximumdepth has not been reached, the sensor client 105 a returns to step 2310a and determines if the first contour 2202 b is detected at the newlyincreased current depth. If the first contour 2202 b is detected at step2310 a, the sensor client 105 a, at step 2316 a, determines a firstregion 2202 c associated with pixel coordinates 2202 d of the detectedcontour 2202 b. In some embodiments, region 2202 c may be determinedusing a method of non-minimal suppression, as described above. In someembodiments, region 2202 c may be determined using an artificial neuralnetwork.

The same or a similar approach—illustrated in steps 2210 b, 2212 b, 2214b, and 2216 b—may be used to determine a second region 2204 c associatedwith pixel coordinates 2204 d of the contour 2204 b. For example, atstep 2310 b, the sensor client 105 a determines whether a second contour2204 b is detected at the current depth. If the contour 2204 b is notdetected, the sensor client 105 a proceeds, at step 2312 b, to anincreased depth (e.g., to depth 2210 c). If the increased depthcorresponds to having reached a maximum depth (e.g., to reaching theinitial depth 2210 a), the process ends because the second contour 2204b was not detected. If the maximum depth has not been reached, thesensor client 105 a returns to step 2310 b and determines if the secondcontour 2204 b is detected at the newly increased current depth. If thesecond contour 2204 b is detected at step 2210 a, the sensor client 105a, at step 2316 a, determines a second region 2204 c associated withpixel coordinates 2204 d of the detected contour 2204 b. In someembodiments, region 2204 c may be determined using a method ofnon-minimal suppression or an artificial neural network, as describedabove.

At step 2318, the sensor client 105 a determines whether criteria aresatisfied for distinguishing the first and second regions determined insteps 2316 a and 2316 b, respectively. For example, the criteria mayinclude one or more requirements. For example, one requirement may bethat the regions 2202 c, 2204 c overlap by less than or equal to athreshold amount (e.g., of about 10%). Another requirement may be thatthe regions 2202 c, 2204 c are within (e.g., bounded by, e.g.,encompassed by) the merged-contour region 2222 (determined at step2306). If the criteria are not satisfied, method 2300 generally ends.

Otherwise, if the criteria are satisfied at step 2318, the method 2300proceeds to steps 2320 and 2322 where the sensor client 105 a associatesthe first region 2202 b with a first pixel position 2202 e of the firstperson 2202 (step 2320) and associates the second region 2204 b with afirst pixel position 2202 e of the first person 2204 (step 2322).Associating the regions 2202 c, 2204 c to pixel positions 2202 e, 2204 emay correspond to storing in a memory pixel coordinates 2202 d, 2204 dof the regions 2202 c, 2204 c and/or an average pixel positioncorresponding to each of the regions 2202 c, 2204 c along with an objectidentifier for the people 2202, 2204.

At step 2324, the sensor client 105 a may transmit the first and secondpixel positions (e.g., as pixel positions 2226) to the server 106. Atstep 2326, the server 106 may apply a homography (e.g., of homographies2230) for the sensor 2202 to the pixel positions to determinecorresponding physical (e.g., global) positions 2228 for the first andsecond people 2202, 2204. Examples of generating and using homographies2230 are described in greater detail above with respect to FIGS. 2-7.

Modifications, additions, or omissions may be made to method 2300depicted in FIG. 23. Method 2300 may include more, fewer, or othersteps. For example, steps may be performed in parallel or in anysuitable order. While at times discussed as system 2200, sensor client22105 a, master server 2208, or components of any of thereof performingsteps, any suitable system or components of the system may perform oneor more steps of the method.

Multi-Sensor Image Tracking on a Local and Global Planes

As described elsewhere in this disclosure (e.g., with respect to FIGS.19-23 above), tracking people (e.g., or other target objects) in space102 using multiple sensors 108 presents several previously unrecognizedchallenges. This disclosure encompasses not only the recognition ofthese challenges but also unique solutions to these challenges. Forinstance, systems and methods are described in this disclosure thattrack people both locally (e.g., by tracking pixel positions in imagesreceived from each sensor 108) and globally (e.g., by tracking physicalpositions on a global plane corresponding to the physical coordinates inthe space 102). Person tracking may be more reliable when performed bothlocally and globally. For example, if a person is “lost” locally (e.g.,if a sensor 108 fails to capture a frame and a person is not detected bythe sensor 108), the person may still be tracked globally based on animage from a nearby sensor 108 (e.g., the angled-view sensor 108 bdescribed with respect to FIG. 22 above), an estimated local position ofthe person determined using a local tracking algorithm, and/or anestimated global position determined using a global tracking algorithm.

As another example, if people appear to merge (e.g., if detectedcontours merge into a single merged contour, as illustrated in view 2216of FIG. 22 above) at one sensor 108, an adjacent sensor 108 may stillprovide a view in which the people are separate entities (e.g., asillustrated in view 2232 of FIG. 22 above). Thus, information from anadjacent sensor 108 may be given priority for person tracking. In someembodiments, if a person tracked via a sensor 108 is lost in the localview, estimated pixel positions may be determined using a trackingalgorithm and reported to the server 106 for global tracking, at leastuntil the tracking algorithm determines that the estimated positions arebelow a threshold confidence level.

FIGS. 24A-C illustrate the use of a tracking subsystem 2400 to track aperson 2402 through the space 102. FIG. 24A illustrates a portion of thetracking system 100 of FIG. 1 when used to track the position of person2402 based on image data generated by sensors 108 a-c. The position ofperson 2402 is illustrated at three different time points: t₁, t₂, andt₃. Each of the sensors 108 a-c is a sensor 108 of FIG. 1, describedabove. Each sensor 108 a-c has a corresponding field-of-view 2404 a-c,which corresponds to the portion of the space 102 viewed by the sensor108 a-c. As shown in FIG. 24A, each field-of-view 2404 a-c overlaps withthat of the adjacent sensor(s) 108 a-c. For example, the adjacentfields-of-view 2404 a-c may overlap by between about 10% and 30%.Sensors 108 a-c generally generate top-view images and transmitcorresponding top-view image feeds 2406 a-c to a tracking subsystem2400.

The tracking subsystem 2400 includes the client(s) 105 and server 106 ofFIG. 1. The tracking system 2400 generally receives top-view image feeds2406 a-c generated by sensors 108 a-c, respectively, and uses thereceived images (see FIG. 24B) to track a physical (e.g., global)position of the person 2402 in the space 102 (see FIG. 24C). Each sensor108 a-c may be coupled to a corresponding sensor client 105 of thetracking subsystem 2400. As such, the tracking subsystem 2400 mayinclude local particle filter trackers 2444 for tracking pixel positionsof person 2402 in images generated by sensors 108 a-b, global particlefilter trackers 2446 for tracking physical positions of person 2402 inthe space 102.

FIG. 24B shows example top-view images 2408 a-c, 2418 a-c, and 2426 a-cgenerated by each of the sensors 108 a-c at times t₁, t₂, and t₃.Certain of the top-view images include representations of the person2402 (i.e., if the person 2402 was in the field-of-view 2404 a-c of thesensor 108 a-c at the time he image 2408 a-c, 2418 a-c, and 2426 a-c wasobtained). For example, at time t₁, images 2408 a-c are generated bysensors 108 a-c, respectively, and provided to the tracking subsystem2400. The tracking subsystem 2400 detects a contour 2410 associated withperson 2402 in image 2408 a. For example, the contour 2410 maycorrespond to a curve outlining the border of a representation of theperson 2402 in image 2408 a (e.g., detected based on color (e.g., RGB)image data at a predefined depth in image 2408 a, as described abovewith respect to FIG. 19). The tracking subsystem 2400 determines pixelcoordinates 2412 a, which are illustrated in this example by thebounding box 2412 b in image 2408 a. Pixel position 2412 c is determinedbased on the coordinates 2412 a. The pixel position 2412 c generallyrefers to the location (i.e., row and column) of the person 2402 in theimage 2408 a. Since the object 2402 is also within the field-of-view2404 b of the second sensor 108 b at t₁ (see FIG. 24A), the trackingsystem also detects a contour 2414 in image 2408 b and determinescorresponding pixel coordinates 2416 a (i.e., associated with boundingbox 2416 b) for the object 2402. Pixel position 2416 c is determinedbased on the coordinates 2416 a. The pixel position 2416 c generallyrefers to the pixel location (i.e., row and column) of the person 2402in the image 2408 b. At time t₁, the object 2402 is not in thefield-of-view 2404 c of the third sensor 108 c (see FIG. 24A).Accordingly, the tracking subsystem 2400 does not determine pixelcoordinates for the object 2402 based on the image 2408 c received fromthe third sensor 108 c.

Turning now to FIG. 24C, the tracking subsystem 2400 (e.g., the server106 of the tacking subsystem 2400) may determine a first global position2438 based on the determined pixel positions 2412 c and 2416 c (e.g.,corresponding to pixel coordinates 2412 a, 2416 a and bounding boxes2412 b, 2416 b, described above). The first global position 2438corresponds to the position of the person 2402 in the space 102, asdetermined by the tracking subsystem 2400. In other words, the trackingsubsystem 2400 uses the pixel positions 2412 c, 2416 c determined viathe two sensors 108 a,b to determine a single physical position 2438 forthe person 2402 in the space 102. For example, a first physical position2412 d may be determined from the pixel position 2412 c associated withbounding box 2412 b using a first homography associating pixelcoordinates in the top-view images generated by the first sensor 108 ato physical coordinates in the space 102. A second physical position2416 d may similarly be determined using the pixel position 2416 cassociated with bounding box 2416 b using a second homographyassociating pixel coordinates in the top-view images generated by thesecond sensor 108 b to physical coordinates in the space 102. In somecases, the tracking subsystem 2400 may compare the distance betweenfirst and second physical positions 2412 d and 2416 d to a thresholddistance 2448 to determine whether the positions 2412 d, 2416 dcorrespond to the same person or different people (see, e.g., step 2620of FIG. 26, described below). The first global position 2438 may bedetermined as an average of the first and second physical positions 2410d, 2414 d. In some embodiments, the global position is determined byclustering the first and second physical positions 2410 d, 2414 d (e.g.,using any appropriate clustering algorithm). The first global position2438 may correspond to (x,y) coordinates of the position of the person2402 in the space 102.

Returning to FIG. 24A, at time t₂, the object 2402 is withinfields-of-view 2404 a and 2404 b corresponding to sensors 108 a,b. Asshown in FIG. 24B, a contour 2422 is detected in image 2418 b andcorresponding pixel coordinates 2424 a, which are illustrated bybounding box 2424 b, are determined. Pixel position 2424 c is determinedbased on the coordinates 2424 a. The pixel position 2424 c generallyrefers to the location (i.e., row and column) of the person 2402 in theimage 2418 b. However, in this example, the tracking subsystem 2400fails to detect, in image 2418 a from sensor 108 a, a contour associatedwith object 2402. This may be because the object 2402 was at the edge ofthe field-of-view 2404 a, because of a lost image frame from feed 2406a, because the position of the person 2402 in the field-of-view 2404 acorresponds to an auto-exclusion zone for sensor 108 a (see FIGS. 19-21and corresponding description above), or because of any othermalfunction of sensor 108 a and/or the tracking subsystem 2400. In thiscase, the tracking subsystem 2400 may locally (e.g., at the particularclient 105 which is coupled to sensor 108 a) estimate pixel coordinates2420 a and/or corresponding pixel position 2420 b for object 2402. Forexample, a local particle filter tracker 2444 for object 2402 in imagesgenerated by sensor 108 a may be used to determine the estimated pixelposition 2420 b.

FIGS. 25A,B illustrate the operation of an example particle filtertracker 2444, 2446 (e.g., for determining estimated pixel position 2420a). FIG. 25A illustrates a region 2500 in pixel coordinates or physicalcoordinates of space 102. For example, region 2500 may correspond to apixel region in an image or to a region in physical space. In a firstzone 2502, an object (e.g., person 2402) is detected at position 2504.The particle filter determines several estimated subsequent positions2506 for the object. The estimated subsequent positions 2506 areillustrated as the dots or “particles” in FIG. 25A and are generallydetermined based on a history of previous positions of the object.Similarly, another zone 2508 shows a position 2510 for another object(or the same object at a different time) along with estimated subsequentpositions 2512 of the “particles” for this object.

For the object at position 2504, the estimated subsequent positions 2506are primarily clustered in a similar area above and to the right ofposition 2504, indicating that the particle filter tracker 2444, 2446may provide a relatively good estimate of a subsequent position.Meanwhile, the estimated subsequent positions 2512 are relativelyrandomly distributed around position 2510 for the object, indicatingthat the particle filter tracker 2444, 2446 may provide a relativelypoor estimate of a subsequent position. FIG. 25B shows a distributionplot 2550 of the particles illustrated in FIG. 25A, which may be used toquantify the quality of an estimated position based on a standarddeviation value (σ).

In FIG. 25B, curve 2552 corresponds to the position distribution ofanticipated positions 2506, and curve 2554 corresponds to the positiondistribution of the anticipated positions 2512. Curve 2554 has to arelatively narrow distribution such that the anticipated positions 2506are primarily near the mean position (μ). For example, the narrowdistribution corresponds to the particles primarily having a similarposition, which in this case is above and to right of position 2504. Incontrast, curve 2554 has a broader distribution, where the particles aremore randomly distributed around the mean position (μ). Accordingly, thestandard deviation of curve 2552 (σ₁) is smaller than the standarddeviation curve 2554 (σ₂). Generally, a standard deviation (e.g., eitherσ₁ or σ₂) may be used as a measure of an extent to which an estimatedpixel position generated by the particle filter tracker 2444, 2446 islikely to be correct. If the standard deviation is less than a thresholdstandard deviation (σ_(threshold)), as is the case with curve 2552 andσ₁, the estimated position generated by a particle filter tracker 2444,2446 may be used for object tracking. Otherwise, the estimated positiongenerally is not used for object tracking.

Referring again to FIG. 24C, the tracking subsystem 2400 (e.g., theserver 106 of tracking subsystem 2400) may determine a second globalposition 2440 for the object 2402 in the space 102 based on theestimated pixel position 2420 b associated with estimated bounding box2420 a in frame 2418 a and the pixel position 2424 c associated withbounding box 2424 b from frame 2418 b. For example, a first physicalposition 2420 c may be determined using a first homography associatingpixel coordinates in the top-view images generated by the first sensor108 a to physical coordinates in the space 102. A second physicalposition 2424 d may be determined using a second homography associatingpixel coordinates in the top-view images generated by the second sensor108 b to physical coordinates in the space 102. The tracking subsystem2400 (i.e., server 106 of the tracking subsystem 2400) may determine thesecond global position 2440 based on the first and second physicalpositions 2420 c, 2424 d, as described above with respect to time t₁.The second global position 2440 may correspond to (x,y) coordinates ofthe person 2402 in the space 102.

Turning back to FIG. 24A, at time t₃, the object 2402 is within thefield-of-view 2404 b of sensor 108 b and the field-of-view 2404 c ofsensor 108 c. Accordingly, these images 2426 b,c may be used to trackperson 2402. FIG. 24B shows that a contour 2428 and corresponding pixelcoordinates 2430 a, pixel region 2430 b, and pixel position 2430 c aredetermined in frame 2426 b from sensor 108 b, while a contour 2432 andcorresponding pixel coordinates 2434 a, pixel region 2434 b, and pixelposition 2434 c are detected in frame 2426 c from sensor 108 c. As shownin FIG. 24C and as described in greater detail above for times t₁ andt₂, the tracking subsystem 2400 may determine a third global position2442 for the object 2402 in the space based on the pixel position 2430 cassociated with bounding box 2430 b in frame 2426 b and the pixelposition 2434 c associated with bounding box 2434 b from frame 2426 c.For example, a first physical position 2430 d may be determined using asecond homography associating pixel coordinates in the top-view imagesgenerated by the second sensor 108 b to physical coordinates in thespace 102. A second physical position 2434 d may be determined using athird homography associating pixel coordinates in the top-view imagesgenerated by the third sensor 108 c to physical coordinates in the space102. The tracking subsystem 2400 may determine the global position 2442based on the first and second physical positions 2430 d, 2434 d, asdescribed above with respect to times t₁ and t₂.

FIG. 26 is a flow diagram illustrating the tracking of person 2402 inspace the 102 based on top-view images (e.g., images 2408 a-c, 2418 a-c,2426 a-c from feeds 2406 a,b, generated by sensors 108 a,b, describedabove. Field-of-view 2404 a of sensor 108 a and field-of-view 2404 b ofsensors 108 b generally overlap by a distance 2602. In one embodiment,distance 2602 may be about 10% to 30% of the fields-of-view 2404 a,b. Inthis example, the tracking subsystem 2400 includes the first sensorclient 105 a, the second sensor client 105 b, and the server 106. Eachof the first and second sensor clients 105 a,b may be a client 105described above with respect to FIG. 1. The first sensor client 105 a iscoupled to the first sensor 108 a and configured to track, based on thefirst feed 2406 a, a first pixel position 2112 c of the person 2402. Thesecond sensor client 105 b is coupled to the second sensor 108 b andconfigured to track, based on the second feed 2406 b, a second pixelposition 2416 c of the same person 2402.

The server 106 generally receives pixel positions from clients 105 a,band tracks the global position of the person 2402 in the space 102. Insome embodiments, the server 106 employs a global particle filtertracker 2446 to track a global physical position of the person 2402 andone or more other people 2604 in the space 102). Tracking people bothlocally (i.e., at the “pixel level” using clients 105 a,b) and globally(i.e., based on physical positions in the space 102) improves trackingby reducing and/or eliminating noise and/or other tracking errors whichmay result from relying on either local tracking by the clients 105 a,bor global tracking by the server 106 alone.

FIG. 26 illustrates a method 2600 implemented by sensor clients 105 a,band server 106. Sensor client 105 a receives the first data feed 2406 afrom sensor 108 a at step 2606 a. The feed may include top-view images(e.g., images 2408 a-c, 2418 a-c, 2426 a-c of FIG. 24). The images maybe color images, depth images, or color-depth images. In an image fromthe feed 2406 a (e.g., corresponding to a certain timestamp), the sensorclient 105 a determines whether a contour is detected at step 2608 a. Ifa contour is detected at the timestamp, the sensor client 105 adetermines a first pixel position 2412 c for the contour at step 2610 a.For instance, the first pixel position 2412 c may correspond to pixelcoordinates associated with a bounding box 2412 b determined for thecontour (e.g., using any appropriate object detection algorithm). Asanother example, the sensor client 105 a may generate a pixel mask thatoverlays the detected contour and determine pixel coordinates of thepixel mask, as described above with respect to step 2104 of FIG. 21.

If a contour is not detected at step 2608 a, a first particle filtertracker 2444 may be used to estimate a pixel position (e.g., estimatedposition 2420 b), based on a history of previous positions of thecontour 2410, at step 2612 a. For example, the first particle filtertracker 2444 may generate a probability-weighted estimate of asubsequent first pixel position corresponding to the timestamp (e.g., asdescribed above with respect to FIGS. 25A,B). Generally, if theconfidence level (e.g., based on a standard deviation) of the estimatedpixel position 2420 b is below a threshold value (e.g., see FIG. 25B andrelated description above), no pixel position is determined for thetimestamp by the sensor client 105 a, and no pixel position is reportedto server 106 for the timestamp. This prevents the waste of processingresources which would otherwise be expended by the server 106 inprocessing unreliable pixel position data. As described below, theserver 106 can often still track person 2402, even when no pixelposition is provided for a given timestamp, using the global particlefilter tracker 2446 (see steps 2626, 2632, and 2636 below).

The second sensor client 105 b receives the second data feed 2406 b fromsensor 108 b at step 2606 b. The same or similar steps to thosedescribed above for sensor client 105 a are used to determine a secondpixel position 2416 c for a detected contour 2414 or estimate a pixelposition based on a second particle filter tracker 2444. At step 2608 b,the sensor client 105 b determines whether a contour 2414 is detected inan image from feed 2406 b at a given timestamp. If a contour 2414 isdetected at the timestamp, the sensor client 105 b determines a firstpixel position 2416 c for the contour 2414 at step 2610 b (e.g., usingany of the approaches described above with respect to step 2610 a). If acontour 2414 is not detected, a second particle filter tracker 2444 maybe used to estimate a pixel position at step 2612 b (e.g., as describedabove with respect to step 2612 a). If the confidence level of theestimated pixel position is below a threshold value (e.g., based on astandard deviation value for the tracker 2444), no pixel position isdetermined for the timestamp by the sensor client 105 b, and no pixelposition is reported for the timestamp to the server 106.

While steps 2606 a,b-2612 a,b are described as being performed by sensorclient 105 a and 105 b, it should be understood that in someembodiments, a single sensor client 105 may receive the first and secondimage feeds 2406 a,b from sensors 108 a,b and perform the stepsdescribed above. Using separate sensor clients 105 a,b for separatesensors 108 a,b or sets of sensors 108 may provide redundancy in case ofclient 105 malfunctions (e.g., such that even if one sensor client 105fails, feeds from other sensors may be processed by otherstill-functioning clients 105).

At step 2614, the server 106 receives the pixel positions 2412 c, 2416 cdetermined by the sensor clients 105 a,b. At step 2616, the server 106may determine a first physical position 2412 d based on the first pixelposition 2412 c determined at step 2610 a or estimated at step 2612 a bythe first sensor client 105 a. For example, the first physical position2412 d may be determined using a first homography associating pixelcoordinates in the top-view images generated by the first sensor 108 ato physical coordinates in the space 102. At step 2618, the server 106may determine a second physical position 2416 d based on the secondpixel position 2416 c determined at step 2610 b or estimated at step2612 b by the first sensor client 105 b. For instance, the secondphysical position 2416 d may be determined using a second homographyassociating pixel coordinates in the top-view images generated by thesecond sensor 108 b to physical coordinates in the space 102.

At step 2620 the server 106 determines whether the first and secondpositions 2412 d, 2416 d (from steps 2616 and 2618) are within athreshold distance 2448 (e.g., of about six inches) of each other. Ingeneral, the threshold distance 2448 may be determined based on one ormore characteristics of the system tracking system 100 and/or the person2402 or another target object being tracked. For example, the thresholddistance 2448 may be based on one or more of the distance of the sensors108 a-b from the object, the size of the object, the fields-of-view 2404a-b, the sensitivity of the sensors 108 a-b, and the like. Accordingly,the threshold distance 2448 may range from just over zero inches togreater than six inches depending on these and other characteristics ofthe tracking system 100.

If the positions 2412 d, 2416 d are within the threshold distance 2448of each other at step 2620, the server 106 determines that the positions2412 d, 2416 d correspond to the same person 2402 at step 2622. In otherwords, the server 106 determines that the person detected by the firstsensor 108 a is the same person detected by the second sensor 108 b.This may occur, at a given timestamp, because of the overlap 2604between field-of-view 2404 a and field-of-view 2404 b of sensors 108 aand 108 b, as illustrated in FIG. 26.

At step 2624, the server 106 determines a global position 2438 (i.e., aphysical position in the space 102) for the object based on the firstand second physical positions from steps 2616 and 2618. For instance,the server 106 may calculate an average of the first and second physicalpositions 2412 d, 2416 d. In some embodiments, the global position 2438is determined by clustering the first and second physical positions 2412d, 2416 d (e.g., using any appropriate clustering algorithm). At step2626, a global particle filter tracker 2446 is used to track the global(e.g., physical) position 2438 of the person 2402. An example of aparticle filter tracker is described above with respect to FIGS. 25A,B.For instance, the global particle filter tracker 2446 may generateprobability-weighted estimates of subsequent global positions atsubsequent times. If a global position 2438 cannot be determined at asubsequent timestamp (e.g., because pixel positions are not availablefrom the sensor clients 105 a,b), the particle filter tracker 2446 maybe used to estimate the position.

If at step 2620 the first and second physical positions 2412 d, 2416 dare not within the threshold distance 2448 from each other, the server106 generally determines that the positions correspond to differentobjects 2402, 2604 at step 2628. In other words, the server 106 maydetermine that the physical positions determined at steps 2616 and 2618are sufficiently different, or far apart, for them to correspond to thefirst person 2402 and a different second person 2604 in the space 102.

At step 2630, the server 106 determines a global position for the firstobject 2402 based on the first physical position 2412 c from step 2616.Generally, in the case of having only one physical position 2412 c onwhich to base the global position, the global position is the firstphysical position 2412 c. If other physical positions are associatedwith the first object (e.g., based on data from other sensors 108, whichfor clarity are not shown in FIG. 26), the global position of the firstperson 2402 may be an average of the positions or determined based onthe positions using any appropriate clustering algorithm, as describedabove. At step 2632, a global particle filter tracker 2446 may be usedto track the first global position of the first person 2402, as is alsodescribed above.

At step 2634, the server 106 determines a global position for the secondperson 2404 based on the second physical position 2416 c from step 2618.Generally, in the case of having only one physical position 2416 c onwhich to base the global position, the global position is the secondphysical position 2416 c. If other physical positions are associatedwith the second object (e.g., based on data from other sensors 108,which not shown in FIG. 26 for clarity), the global position of thesecond person 2604 may be an average of the positions or determinedbased on the positions using any appropriate clustering algorithm. Atstep 2636, a global particle filter tracker 2446 is used to track thesecond global position of the second object, as described above.

Modifications, additions, or omissions may be made to the method 2600described above with respect to FIG. 26. The method may include more,fewer, or other steps. For example, steps may be performed in parallelor in any suitable order. While at times discussed as a trackingsubsystem 2400, sensor clients 105 a,b, server 106, or components of anythereof performing steps, any suitable system or components of thesystem may perform one or more steps of the method 2600.

Candidate Lists

When the tracking system 100 is tracking people in the space 102, it maybe challenging to reliably identify people under certain circumstancessuch as when they pass into or near an auto-exclusion zone (see FIGS.19-21 and corresponding description above), when they stand near anotherperson (see FIGS. 22-23 and corresponding description above), and/orwhen one or more of the sensors 108, client(s) 105, and/or server 106malfunction. For instance, after a first person becomes close to or evencomes into contact with (e.g., “collides” with) a second person, it maydifficult to determine which person is which (e.g., as described abovewith respect to FIG. 22). Conventional tracking systems may usephysics-based tracking algorithms in an attempt to determine whichperson is which based on estimated trajectories of the people (e.g.,estimated as though the people are marbles colliding and changingtrajectories according to a conservation of momentum, or the like).However, identities of people may be more difficult to track reliably,because movements may be random. As described above, the tracking system100 may employ particle filter tracking for improved tracking of peoplein the space 102 (see e.g., FIGS. 24-26 and the correspondingdescription above). However, even with these advancements, theidentities of people being tracked may be difficult to determine atcertain times. This disclosure particularly encompasses the recognitionthat positions of people who are shopping in a store (i.e., moving abouta space, selecting items, and picking up the items) are difficult orimpossible to track using previously available technology becausemovement of these people is random and does not follow a readily definedpattern or model (e.g., such as the physics-based models of previousapproaches). Accordingly, there is a lack of tools for reliably andefficiently tracking people (e.g., or other target objects).

This disclosure provides a solution to the problems of previoustechnology, including those described above, by maintaining a record,which is referred to in this disclosure as a “candidate list,” ofpossible person identities, or identifiers (i.e., the usernames, accountnumbers, etc. of the people being tracked), during tracking. A candidatelist is generated and updated during tracking to establish the possibleidentities of each tracked person. Generally, for each possible identityor identifier of a tracked person, the candidate list also includes aprobability that the identity, or identifier, is believed to be correct.The candidate list is updated following interactions (e.g., collisions)between people and in response to other uncertainty events (e.g., a lossof sensor data, imaging errors, intentional trickery, etc.).

In some cases, the candidate list may be used to determine when a personshould be re-identified (e.g., using methods described in greater detailbelow with respect to FIGS. 29-32). Generally, re-identification isappropriate when the candidate list of a tracked person indicates thatthe person's identity is not sufficiently well known (e.g., based on theprobabilities stored in the candidate list being less than a thresholdvalue). In some embodiments, the candidate list is used to determinewhen a person is likely to have exited the space 102 (i.e., with atleast a threshold confidence level), and an exit notification is onlysent to the person after there is high confidence level that the personhas exited (see, e.g., view 2730 of FIG. 27, described below). Ingeneral, processing resources may be conserved by only performingpotentially complex person re-identification tasks when a candidate listindicates that a person's identity is no longer known according topre-established criteria.

FIG. 27 is a flow diagram illustrating how identifiers 2701 a-cassociated with tracked people (e.g., or any other target object) may beupdated during tracking over a period of time from an initial time t₀ toa final time t₅ by tracking system 100. People may be tracked usingtracking system 100 based on data from sensors 108, as described above.FIG. 27 depicts a plurality of views 2702, 2716, 2720, 2724, 2728, 2730at different time points during tracking. In some embodiments, views2702, 2716, 2720, 2724, 2728, 2730 correspond to a local frame view(e.g., as described above with respect to FIG. 22) from a sensor 108with coordinates in units of pixels (e.g., or any other appropriate unitfor the data type generated by the sensor 108). In other embodiments,views 2702, 2716, 2720, 2724, 2728, 2730 correspond to global views ofthe space 102 determined based on data from multiple sensors 108 withcoordinates corresponding to physical positions in the space (e.g., asdetermined using the homographies described in greater detail above withrespect to FIGS. 2-7). For clarity and conciseness, the example of FIG.27 is described below in terms of global views of the space 102 (i.e., aview corresponding to the physical coordinates of the space 102).

The tracked object regions 2704, 2708, 2712 correspond to regions of thespace 102 associated with the positions of corresponding people (e.g.,or any other target object) moving through the space 102. For example,each tracked object region 2704, 2708, 2712 may correspond to adifferent person moving about in the space 102. Examples of determiningthe regions 2704, 2708, 2712 are described above, for example, withrespect to FIGS. 21, 22, and 24. As one example, the tracked objectregions 2704, 2708, 2712 may be bounding boxes identified forcorresponding objects in the space 102. As another example, trackedobject regions 2704, 2708, 2712 may correspond to pixel masks determinedfor contours associated with the corresponding objects in the space 102(see, e.g., step 2104 of FIG. 21 for a more detailed description of thedetermination of a pixel mask). Generally, people may be tracked in thespace 102 and regions 2704, 2708, 2712 may be determined using anyappropriate tracking and identification method.

View 2702 at initial time t₀ includes a first tracked object region2704, a second tracked object region 2708, and a third tracked objectregion 2712. The view 2702 may correspond to a representation of thespace 102 from a top view with only the tracked object regions 2704,2708, 2712 shown (i.e., with other objects in the space 102 omitted). Attime t₀, the identities of all of the people are generally known (e.g.,because the people have recently entered the space 102 and/or becausethe people have not yet been near each other). The first tracked objectregion 2704 is associated with a first candidate list 2706, whichincludes a probability (P_(A)=100%) that the region 2704 (or thecorresponding person being tracked) is associated with a firstidentifier 2701 a. The second tracked object region 2708 is associatedwith a second candidate list 2710, which includes a probability(P_(B)=100%) that the region 2708 (or the corresponding person beingtracked) is associated with a second identifier 2701 b. The thirdtracked object region 2712 is associated with a third candidate list2714, which includes a probability (P_(C)=100%) that the region 2712 (orthe corresponding person being tracked) is associated with a thirdidentifier 2701 c. Accordingly, at time t₁, the candidate lists 2706,2710, 2714 indicate that the identity of each of the tracked objectregions 2704, 2708, 2712 is known with all probabilities having a valueof one hundred percent.

View 2716 shows positions of the tracked objects 2704, 2708, 2712 at afirst time t₁, which is after the initial time t₀. At time t₁, thetracking system detects an event which may cause the identities of thetracked object regions 2704, 2708 to be less certain. In this example,the tracking system 100 detects that the distance 2718 a between thefirst object region 274 and the second object region 2708 is less thanor equal to a threshold distance 2718 b. Because the tracked objectregions were near each other (i.e., within the threshold distance 2718b), there is a non-zero probability that the regions may bemisidentified during subsequent times. The threshold distance 2718 b maybe any appropriate distance, as described above with respect to FIG. 22.For example, the tracking system 100 may determine that the first objectregion 2704 is within the threshold distance 2718 b of the second objectregion 2708 by determining first coordinates of the first object region2704, determining second coordinates of the second object region 2708,calculating a distance 2718 a, and comparing distance 2718 a to thethreshold distance 2718 b. In some embodiments, the first and secondcoordinates correspond to pixel coordinates in an image capturing thefirst and second people, and the distance 2718 a corresponds to a numberof pixels between these pixel coordinates. For example, as illustratedin view 2716 of FIG. 27, the distance 2718 a may correspond to the pixeldistance between centroids of the tracked object regions 2704, 2708. Inother embodiments, the first and second coordinates correspond tophysical, or global, coordinates in the space 102, and the distance 2718a corresponds to a physical distance (e.g., in units of length, such asinches). For example, physical coordinates may be determined using thehomographies described in greater detail above with respect to FIGS.2-7.

After detecting that the identities of regions 2704, 2708 are lesscertain (i.e., that the first object region 2704 is within the thresholddistance 2718 b of the second object region 2708), the tracking system100 determines a probability 2717 that the first tracked object region2704 switched identifiers 2701 a-c with the second tracked object region2708. For example, when two contours become close in an image, there isa chance that the identities of the contours may be incorrect duringsubsequent tracking (e.g., because the tracking system 100 may assignthe wrong identifier 2701 a-c to the contours between frames). Theprobability 2717 that the identifiers 2701 a-c switched may bedetermined, for example, by accessing a predefined probability value(e.g., of 50%). In other cases, the probability 2717 may be based on thedistance 2718 a between the object regions 2704, 2708. For example, asthe distance 2718 decreases, the probability 2717 that the identifiers2701 a-c switched may increase. In the example of FIG. 27, thedetermined probability 2717 is 20%, because the object regions 2704,2708 are relatively far apart but there is some overlap between theregions 2704, 2708.

In some embodiments, the tracking system 100 may determine a relativeorientation between the first object region 2704 and the second objectregion 2708, and the probability 2717 that the object regions 2704, 2708switched identifiers 2701 a-c may be based on this relative orientation.The relative orientation may correspond to an angle between a directiona person associated with the first region 2704 is facing and a directiona person associated with the second region 2708 is facing. For example,if the angle between the directions faced by people associated withfirst and second regions 2704, 2708 is near 180° (i.e., such that thepeople are facing in opposite directions), the probability 2717 thatidentifiers 2701 a-c switched may be decreased because this case maycorrespond to one person accidentally backing into the other person.

Based on the determined probability 2717 that the tracked object regions2704, 2708 switched identifiers 2701 a-c (e.g., 20% in this example),the tracking system 100 updates the first candidate list 2706 for thefirst object region 2704. The updated first candidate list 2706 includesa probability (P_(A)=80%) that the first region 2704 is associated withthe first identifier 2701 a and a probability (P_(B)=20%) that the firstregion 2704 is associated with the second identifier 2701 b. The secondcandidate list 2710 for the second object region 2708 is similarlyupdated based on the probability 2717 that the first object region 2704switched identifiers 2701 a-c with the second object region 2708. Theupdated second candidate list 2710 includes a probability (P_(A)=20%)that the second region 2708 is associated with the first identifier 2701a and a probability (P_(B)=80%) that the second region 2708 isassociated with the second identifier 2701 b.

View 2720 shows the object regions 2704, 2708, 2712 at a second timepoint t₂, which follows time t₁. At time t₂, a first personcorresponding to the first tracked region 2704 stands close to a thirdperson corresponding to the third tracked region 2712. In this examplecase, the tracking system 100 detects that the distance 2722 between thefirst object region 2704 and the third object region 2712 is less thanor equal to the threshold distance 2718 b (i.e., the same thresholddistance 2718 b described above with respect to view 2716). Afterdetecting that the first object region 2704 is within the thresholddistance 2718 b of the third object region 2712, the tracking system 100determines a probability 2721 that the first tracked object region 2704switched identifiers 2701 a-c with the third tracked object region 2712.As described above, the probability 2721 that the identifiers 2701 a-cswitched may be determined, for example, by accessing a predefinedprobability value (e.g., of 50%). In some cases, the probability 2721may be based on the distance 2722 between the object regions 2704, 2712.For example, since the distance 2722 is greater than distance 2718 a(from view 2716, described above), the probability 2721 that theidentifiers 2701 a-c switched may be greater at time t₁ than at time t₂.In the example of view 2720 of FIG. 27, the determined probability 2721is 10% (which is smaller than the switching probability 2717 of 20%determined at time t₁).

Based on the determined probability 2721 that the tracked object regions2704, 2712 switched identifiers 2701 a-c (e.g., of 10% in this example),the tracking system 100 updates the first candidate list 2706 for thefirst object region 2704. The updated first candidate list 2706 includesa probability (P_(A)=73%) that the first object region 2704 isassociated with the first identifier 2701 a, a probability (P_(B)=17%)that the first object region 2704 is associated with the secondidentifier 2701 b, and a probability (P_(C)=10%) that the first objectregion 2704 is associated with the third identifier 2701 c. The thirdcandidate list 2714 for the third object region 2712 is similarlyupdated based on the probability 2721 that the first object region 2704switched identifiers 2701 a-c with the third object region 2712. Theupdated third candidate list 2714 includes a probability (P_(A)=7%) thatthe third object region 2712 is associated with the first identifier2701 a, a probability (P_(B)=3%) that the third object region 2712 isassociated with the second identifier 2701 b, and a probability(P_(C)=90%) that the third object region 2712 is associated with thethird identifier 2701 c. Accordingly, even though the third objectregion 2712 never interacted with (e.g., came within the thresholddistance 2718 b of) the second object region 2708, there is still anon-zero probability (P_(B)=3%) that the third object region 2712 isassociated with the second identifier 2701 b, which was originallyassigned (at time t₀) to the second object region 2708. In other words,the uncertainty in object identity that was detected at time t₁ ispropagated to the third object region 2712 via the interaction withregion 2704 at time t₂. This unique “propagation effect” facilitatesimproved object identification and can be used to narrow the searchspace (e.g., the number of possible identifiers 2701 a-c that may beassociated with a tracked object region 2704, 2708, 2712) when objectre-identification is needed (as described in greater detail below andwith respect to FIGS. 29-32).

View 2724 shows third object region 2712 and an unidentified objectregion 2726 at a third time point t₃, which follows time t₂. At time t₃,the first and second people associated with regions 2704, 2708 come intocontact (e.g., or “collide”) or are otherwise so close to one anotherthat the tracking system 100 cannot distinguish between the people. Forexample, contours detected for determining the first object region 2704and the second object region 2708 may have merged resulting in thesingle unidentified object region 2726. Accordingly, the position ofobject region 2726 may correspond to the position of one or both ofobject regions 2704 and 2708. At time t₃, the tracking system 100 maydetermine that the first and second object regions 2704, 2708 are nolonger detected because a first contour associated with the first objectregion 2704 is merged with a second contour associated with the secondobject region 2708.

The tracking system 100 may wait until a subsequent time t₄ (shown inview 2728) when the first and second object regions 2704, 2708 are againdetected before the candidate lists 2706, 2710 are updated. Time t₄generally corresponds to a time when the first and second peopleassociated with regions 2704, 2708 have separated from each other suchthat each person can be tracked in the space 102. Following a mergingevent such as is illustrated in view 2724, the probability 2725 thatregions 2704 and 2708 have switched identifiers 2701 a-c may be 50%. Attime t₄, updated candidate list 2706 includes an updated probability(P_(A)=60%) that the first object region 2704 is associated with thefirst identifier 2701 a, an updated probability (P_(B)=35%) that thefirst object region 2704 is associated with the second identifier 2701b, and an updated probability (P_(C)=5%) that the first object region2704 is associated with the third identifier 2701 c. Updated candidatelist 2710 includes an updated probability (P_(A)=33%) that the secondobject region 2708 is associated with the first identifier 2701 a, anupdated probability (P_(B)=62%) that the second object region 2708 isassociated with the second identifier 2701 b, and an updated probability(P_(C)=5%) that the second object region 2708 is associated with thethird identifier 2701 c. Candidate list 2714 is unchanged.

Still referring to view 2728, the tracking system 100 may determine thata highest value probability of a candidate list is less than a thresholdvalue (e.g., P_(threshold)=70%). In response to determining that thehighest probability of the first candidate list 2706 is less than thethreshold value, the corresponding object region 2704 may bere-identified (e.g., using any method of re-identification described inthis disclosure, for example, with respect to FIGS. 29-32). Forinstance, the first object region 2704 may be re-identified because thehighest probability (P_(A)=60%) is less than the threshold probability(P_(threshold)=70%). The tracking system 100 may extract features, ordescriptors, associated with observable characteristics of the firstperson (or corresponding contour) associated with the first objectregion 2704. The observable characteristics may be a height of theobject (e.g., determined from depth data received from a sensor), acolor associated with an area inside the contour (e.g., based on colorimage data from a sensor 108), a width of the object, an aspect ratio(e.g., width/length) of the object, a volume of the object (e.g., basedon depth data from sensor 108), or the like. Examples of otherdescriptors are described in greater detail below with respect to FIG.30. As described in greater detail below, a texture feature (e.g.,determined using a local binary pattern histogram (LBPH) algorithm) maybe calculated for the person. Alternatively or additionally, anartificial neural network may be used to associate the person with thecorrect identifier 2701 a-c (e.g., as described in greater detail belowwith respect to FIG. 29-32).

Using the candidate lists 2706, 2710, 2714 may facilitate more efficientre-identification than was previously possible because, rather thanchecking all possible identifiers 2701 a-c (e.g., and other identifiersof people in space 102 not illustrated in FIG. 27) for a region 2704,2708, 2712 that has an uncertain identity, the tracking system 100 mayidentify a subset of all the other identifiers 2701 a-c that are mostlikely to be associated with the unknown region 2704, 2708, 2712 andonly compare descriptors of the unknown region 2704, 2708, 2712 todescriptors associated with the subset of identifiers 2701 a-c. In otherwords, if the identity of a tracked person is not certain, the trackingsystem 100 may only check to see if the person is one of the few peopleindicated in the person's candidate list, rather than comparing theunknown person to all of the people in the space 102. For example, onlyidentifiers 2701 a-c associated with a non-zero probability, or aprobability greater than a threshold value, in the candidate list 2706are likely to be associated with the correct identifier 2701 a-c of thefirst region 2704. In some embodiments, the subset may includeidentifiers 2701 a-c from the first candidate list 2706 withprobabilities that are greater than a threshold probability value (e.g.,of 10%). Thus, the tracking system 100 may compare descriptors of theperson associated with region 2704 to predetermined descriptorsassociated with the subset. As described in greater detail below withrespect to FIGS. 29-32, the predetermined features (or descriptors) maybe determined when a person enters the space 102 and associated with theknown identifier 2701 a-c of the person during the entrance time period(i.e., before any events may cause the identity of the person to beuncertain. In the example of FIG. 27, the object region 2708 may also bere-identified at or after time t₄ because the highest probabilityP_(B)=62% is less than the example threshold probability of 70%.

View 2730 corresponds to a time t₅ at which only the person associatedwith object region 2712 remains within the space 102. View 2730illustrates how the candidate lists 2706, 2710, 2714 can be used toensure that people only receive an exit notification 2734 when thesystem 100 is certain the person has exited the space 102. In theseembodiments, the tracking system 100 may be configured to transmit anexit notification 2734 to devices associated with these people when theprobability that a person has exited the space 102 is greater than anexit threshold (e.g., P_(exit)=95% or greater).

An exit notification 2734 is generally sent to the device of a personand includes an acknowledgement that the tracking system 100 hasdetermined that the person has exited the space 102. For example, if thespace 102 is a store, the exit notification 2734 provides a confirmationto the person that the tracking system 100 knows the person has exitedthe store and is, thus, no longer shopping. This may provide assuranceto the person that the tracking system 100 is operating properly and isno longer assigning items to the person or incorrectly charging theperson for items that he/she did not intend to purchase.

As people exit the space 102, the tracking system 100 may maintain arecord 2732 of exit probabilities to determine when an exit notification2734 should be sent. In the example of FIG. 27, at time t₅ (shown inview 2730), the record 2732 includes an exit probability(P_(A,exit)=93%) that a first person associated with the first objectregion 2704 has exited the space 102. Since P_(A,exit) is less than theexample threshold exit probability of 95%, an exit notification 2734would not be sent to the first person (e.g., to his/her device). Thus,even though the first object region 2704 is no longer detected in thespace 102, an exit notification 2734 is not sent, because there is stilla chance that the first person is still in the space 102 (i.e., becauseof identity uncertainties that are captured and recorded via thecandidate lists 2706, 2710, 2714). This prevents a person from receivingan exit notification 2734 before he/she has exited the space 102. Therecord 2732 includes an exit probability (P_(B,exit)=97%) that thesecond person associated with the second object region 2708 has exitedthe space 102. Since P_(B,exit) is greater than the threshold exitprobability of 95%, an exit notification 2734 is sent to the secondperson (e.g., to his/her device). The record 2732 also includes an exitprobability (P_(C,exit)=10%) that the third person associated with thethird object region 2712 has exited the space 102. Since P_(C,exit) isless than the threshold exit probability of 95%, an exit notification2734 is not sent to the third person (e.g., to his/her device).

FIG. 28 is a flowchart of a method 2800 for creating and/or maintainingcandidate lists 2706, 2710, 2714 by tracking system 100. Method 2800generally facilitates improved identification of tracked people (e.g.,or other target objects) by maintaining candidate lists 2706, 2710, 2714which, for a given tracked person, or corresponding tracked objectregion (e.g., region 2704, 2708, 2712), include possible identifiers2701 a-c for the object and a corresponding probability that eachidentifier 2701 a-c is correct for the person. By maintaining candidatelists 2706, 2710, 2714 for tracked people, the people may be moreeffectively and efficiently identified during tracking. For example,costly person re-identification (e.g., in terms of system resourcesexpended) may only be used when a candidate list indicates that aperson's identity is sufficiently uncertain.

Method 2800 may begin at step 2802 where image frames are received fromone or more sensors 108. At step 2804, the tracking system 100 uses thereceived frames to track objects in the space 102. In some embodiments,tracking is performed using one or more of the unique tools described inthis disclosure (e.g., with respect to FIGS. 24-26). However, ingeneral, any appropriate method of sensor-based object tracking may beemployed.

At step 2806, the tracking system 100 determines whether a first personis within a threshold distance 2718 b of a second person. This case maycorrespond to the conditions shown in view 2716 of FIG. 27, describedabove, where first object region 2704 is distance 2718 a away fromsecond object region 2708. As described above, the distance 2718 a maycorrespond to a pixel distance measured in a frame or a physicaldistance in the space 102 (e.g., determined using a homographyassociating pixel coordinates to physical coordinates in the space 102).If the first and second people are not within the threshold distance2718 b of each other, the system 100 continues tracking objects in thespace 102 (i.e., by returning to step 2804).

However, if the first and second people are within the thresholddistance 2718 b of each other, method 2800 proceeds to step 2808, wherethe probability 2717 that the first and second people switchedidentifiers 2701 a-c is determined. As described above, the probability2717 that the identifiers 2701 a-c switched may be determined, forexample, by accessing a predefined probability value (e.g., of 50%). Insome embodiments, the probability 2717 is based on the distance 2718 abetween the people (or corresponding object regions 2704, 2708), asdescribed above. In some embodiments, as described above, the trackingsystem 100 determines a relative orientation between the first personand the second person, and the probability 2717 that the people (orcorresponding object regions 2704, 2708) switched identifiers 2701 a-cis determined, at least in part, based on this relative orientation.

At step 2810, the candidate lists 2706, 2710 for the first and secondpeople (or corresponding object regions 2704, 2708) are updated based onthe probability 2717 determined at step 2808. For instance, as describedabove, the updated first candidate list 2706 may include a probabilitythat the first object is associated with the first identifier 2701 a anda probability that the first object is associated with the secondidentifier 2701 b. The second candidate list 2710 for the second personis similarly updated based on the probability 2717 that the first objectswitched identifiers 2701 a-c with the second object (determined at step2808). The updated second candidate list 2710 may include a probabilitythat the second person is associated with the first identifier 2701 aand a probability that the second person is associated with the secondidentifier 2701 b.

At step 2812, the tracking system 100 determines whether the firstperson (or corresponding region 2704) is within a threshold distance2718 b of a third object (or corresponding region 2712). This case maycorrespond, for example, to the conditions shown in view 2720 of FIG.27, described above, where first object region 2704 is distance 2722away from third object region 2712. As described above, the thresholddistance 2718 b may correspond to a pixel distance measured in a frameor a physical distance in the space 102 (e.g., determined using anappropriate homography associating pixel coordinates to physicalcoordinates in the space 102).

If the first and third people (or corresponding regions 2704 and 2712)are within the threshold distance 2718 b of each other, method 2800proceeds to step 2814, where the probability 2721 that the first andthird people (or corresponding regions 2704 and 2712) switchedidentifiers 2701 a-c is determined. As described above, this probability2721 that the identifiers 2701 a-c switched may be determined, forexample, by accessing a predefined probability value (e.g., of 50%). Theprobability 2721 may also or alternatively be based on the distance 2722between the objects 2727 and/or a relative orientation of the first andthird people, as described above. At step 2816, the candidate lists2706, 2714 for the first and third people (or corresponding regions2704, 2712) are updated based on the probability 2721 determined at step2808. For instance, as described above, the updated first candidate list2706 may include a probability that the first person is associated withthe first identifier 2701 a, a probability that the first person isassociated with the second identifier 2701 b, and a probability that thefirst object is associated with the third identifier 2701 c. The thirdcandidate list 2714 for the third person is similarly updated based onthe probability 2721 that the first person switched identifiers with thethird person (i.e., determined at step 2814). The updated thirdcandidate list 2714 may include, for example, a probability that thethird object is associated with the first identifier 2701 a, aprobability that the third object is associated with the secondidentifier 2701 b, and a probability that the third object is associatedwith the third identifier 2701 c. Accordingly, if the steps of method2800 proceed in the example order illustrated in FIG. 28, the candidatelist 2714 of the third person includes a non-zero probability that thethird object is associated with the second identifier 2701 b, which wasoriginally associated with the second person.

If, at step 2812, the first and third people (or corresponding regions2704 and 2712) are not within the threshold distance 2718 b of eachother, the system 100 generally continues tracking people in the space102. For example, the system 100 may proceed to step 2818 to determinewhether the first person is within a threshold distance of an n^(th)person (i.e., some other person in the space 102). At step 2820, thesystem 100 determines the probability that the first and n^(th) peopleswitched identifiers 2701 a-c, as described above, for example, withrespect to steps 2808 and 2814. At step 2822, the candidate lists forthe first and n^(th) people are updated based on the probabilitydetermined at step 2820, as described above, for example, with respectto steps 2810 and 2816 before method 2800 ends. If, at step 2818, thefirst person is not within the threshold distance of the n^(th) person,the method 2800 proceeds to step 2824.

At step 2824, the tracking system 100 determines if a person has exitedthe space 102. For instance, as described above, the tracking system 100may determine that a contour associated with a tracked person is nolonger detected for at least a threshold time period (e.g., of about 30seconds or more). The system 100 may additionally determine that aperson exited the space 102 when a person is no longer detected and alast determined position of the person was at or near an exit position(e.g., near a door leading to a known exit from the space 102). If aperson has not exited the space 102, the tracking system 100 continuesto track people (e.g., by returning to step 2802).

If a person has exited the space 102, the tracking system 100 calculatesor updates record 2732 of probabilities that the tracked objects haveexited the space 102 at step 2826. As described above, each exitprobability of record 2732 generally corresponds to a probability that aperson associated with each identifier 2701 a-c has exited the space102. At step 2828, the tracking system 100 determines if a combined exitprobability in the record 2732 is greater than a threshold value (e.g.,of 95% or greater). If a combined exit probability is not greater thanthe threshold, the tracking system 100 continues to track objects (e.g.,by continuing to step 2818).

If an exit probability from record 2732 is greater than the threshold, acorresponding exit notification 2734 may be sent to the person linked tothe identifier 2701 a-c associated with the probability at step 2830, asdescribed above with respect to view 2730 of FIG. 27. This may preventor reduce instances where an exit notification 2734 is sent prematurelywhile an object is still in the space 102. For example, it may bebeneficial to delay sending an exit notification 2734 until there is ahigh certainty that the associated person is no longer in the space 102.In some cases, several tracked people must exit the space 102 before anexit probability in record 2732 for a given identifier 2701 a-c issufficiently large for an exit notification 2734 to be sent to theperson (e.g., to a device associated with the person).

Modifications, additions, or omissions may be made to method 2800depicted in FIG. 28. Method 2800 may include more, fewer, or othersteps. For example, steps may be performed in parallel or in anysuitable order. While at times discussed as tracking system 100 orcomponents thereof performing steps, any suitable system or componentsof the system may perform one or more steps of the method 2800.

Person Re-Identification

As described above, in some cases, the identity of a tracked person canbecome unknown (e.g., when the people become closely spaced or“collide”, or when the candidate list of a person indicates the person'sidentity is not known, as described above with respect to FIGS. 27-28),and the person may need to be re-identified. This disclosurecontemplates a unique approach to efficiently and reliablyre-identifying people by the tracking system 100. For example, ratherthan relying entirely on resource-expensive machine learning-basedapproaches to re-identify people, a more efficient and speciallystructured approach may be used where “lower-cost” descriptors relatedto observable characteristics (e.g., height, color, width, volume, etc.)of people are used first for person re-identification. “Higher-cost”descriptors (e.g., determined using artificial neural network models)are only used when the lower-cost methods cannot provide reliableresults. For instance, in some embodiments, a person may first bere-identified based on his/her height, hair color, and/or shoe color.However, if these descriptors are not sufficient for reliablyre-identifying the person (e.g., because other people being tracked havesimilar characteristics), progressively higher-level approaches may beused (e.g., involving artificial neural networks that are trained torecognize people) which may be more effective at person identificationbut which generally involve the use of more processing resources.

As an example, each person's height may be used initially forre-identification. However, if another person in the space 102 has asimilar height, a height descriptor may not be sufficient forre-identifying the people (e.g., because it is not possible todistinguish between people with a similar heights based on heightalone), and a higher-level approach may be used (e.g., using a textureoperator or an artificial neural network to characterize the person). Insome embodiments, if the other person with a similar height has neverinteracted with the person being re-identified (e.g., as recorded ineach person's candidate list—see FIG. 27 and corresponding descriptionabove), height may still be an appropriate feature for re-identifyingthe person (e.g., because the other person with a similar height is notassociated with a candidate identity of the person being re-identified).

FIG. 29 illustrates a tracking subsystem 2900 configured to track people(e.g., and/or other target objects) based on sensor data 2904 receivedfrom one or more sensors 108. In general, the tracking subsystem 2900may include one or both of the server 106 and the client(s) 105 of FIG.1, described above. Tracking subsystem 2900 may be implemented using thedevice 3800 described below with respect to FIG. 38. Tracking subsystem2900 may track object positions 2902, over a period of time using sensordata 2904 (e.g., top-view images) generated by at least one of sensors108. Object positions 2902 may correspond to local pixel positions(e.g., pixel positions 2226, 2234 of FIG. 22) determined at a singlesensor 108 and/or global positions corresponding to physical positions(e.g., positions 2228 of FIG. 22) in the space 102 (e.g., using thehomographies described above with respect to FIGS. 2-7). In some cases,object positions 2902 may correspond to regions detected in an image, orin the space 102, that are associated with the location of acorresponding person (e.g., regions 2704, 2708, 2712 of FIG. 27,described above). People may be tracked and corresponding positions 2902may be determined, for example, based on pixel coordinates of contoursdetected in top-view images generated by sensor(s) 108. Examples ofcontour-based detection and tracking are described above, for example,with respect to FIGS. 24 and 27. However, in general, any appropriatemethod of sensor-based tracking may be used to determine positions 2902.

For each object position 2902, the subsystem 2900 maintains acorresponding candidate list 2906 (e.g., as described above with respectto FIG. 27). The candidate lists 2906 are generally used to maintain arecord of the most likely identities of each person being tracked (i.e.,associated with positions 2902). Each candidate list 2906 includesprobabilities which are associated with identifiers 2908 of people thathave entered the space 102. The identifiers 2908 may be any appropriaterepresentation (e.g., an alphanumeric string, or the like) foridentifying a person (e.g., a username, name, account number, or thelike associated with the person being tracked). In some embodiments, theidentifiers 2908 may be anonymized (e.g., using hashing or any otherappropriate anonymization technique).

Each of the identifiers 2908 is associated with one or morepredetermined descriptors 2910. The predetermined descriptors 2910generally correspond to information about the tracked people that can beused to re-identify the people when necessary (e.g., based on thecandidate lists 2906). The predetermined descriptors 2910 may includevalues associated with observable and/or calculated characteristics ofthe people associated with the identifiers 2908. For instance, thedescriptors 2910 may include heights, hair colors, clothing colors, andthe like. As described in greater detail below, the predetermineddescriptors 2910 are generally determined by the tracking subsystem 2900during an initial time period (e.g., when a person associated with agiven tracked position 2902 enters the space) and are used tore-identify people associated with tracked positions 2902 when necessary(e.g., based on candidate lists 2906).

When re-identification is needed (or periodically during tracking) for agiven person at position 2902, the tracking subsystem 2900 may determinemeasured descriptors 2912 for the person associated with the position2902. FIG. 30 illustrates the determination of descriptors 2910, 2912based on a top-view depth image 3002 received from a sensor 108. Arepresentation 2904 a of a person corresponding to the tracked objectposition 2902 is observable in the image 3002. The tracking subsystem2900 may detect a contour 3004 b associated with the representation 3004a. The contour 3004 b may correspond to a boundary of the representation3004 a (e.g., determined at a given depth in image 3002). Trackingsubsystem 2900 generally determines descriptors 2910, 2912 based on therepresentation 3004 a and/or the contour 3004 b. In some cases, therepresentation 3004 b appears within a predefined region-of-interest3006 of the image 3002 in order for descriptors 2910, 2912 to bedetermined by the tracking subsystem 2900. This may facilitate morereliable descriptor 2910, 2912 determination, for example, becausedescriptors 2910, 2912 may be more reproducible and/or reliable when theperson being imaged is located in the portion of the sensor'sfield-of-view that corresponds to this region-of-interest 3006. Forexample, descriptors 2910, 2912 may have more consistent values when theperson is imaged within the region-of-interest 3006.

Descriptors 2910, 2912 determined in this manner may include, forexample, observable descriptors 3008 and calculated descriptors 3010.For example, the observable descriptors 3008 may correspond tocharacteristics of the representation 3004 a and/or contour 3004 b whichcan be extracted from the image 3002 and which correspond to observablefeatures of the person. Examples of observable descriptors 3008 includea height descriptor 3012 (e.g., a measure of the height in pixels orunits of length) of the person based on representation 3004 a and/orcontour 3004 b), a shape descriptor 3014 (e.g., width, length, aspectratio, etc.) of the representation 3004 a and/or contour 3004 b, avolume descriptor 3016 of the representation 3004 a and/or contour 3004b, a color descriptor 3018 of representation 3004 a (e.g., a color ofthe person's hair, clothing, shoes, etc.), an attribute descriptor 3020associated with the appearance of the representation 3004 a and/orcontour 3004 b (e.g., an attribute such as “wearing a hat,” “carrying achild,” “pushing a stroller or cart,”), and the like.

In contrast to the observable descriptors 3008, the calculateddescriptors 3010 generally include values (e.g., scalar or vectorvalues) which are calculated using the representation 3004 a and/orcontour 3004 b and which do not necessarily correspond to an observablecharacteristic of the person. For example, the calculated descriptors3010 may include image-based descriptors 3022 and model-baseddescriptors 3024. Image-based descriptors 3022 may, for example, includeany descriptor values (i.e., scalar and/or vector values) calculatedfrom image 3002. For example, a texture operator such as a local binarypattern histogram (LBPH) algorithm may be used to calculate a vectorassociated with the representation 3004 a. This vector may be stored asa predetermined descriptor 2910 and measured at subsequent times as adescriptor 2912 for re-identification. Since the output of a textureoperator, such as the LBPH algorithm may be large (i.e., in terms of theamount of memory required to store the output), it may be beneficial toselect a subset of the output that is most useful for distinguishingpeople. Accordingly, in some cases, the tracking subsystem 2900 mayselect a portion of the initial data vector to include in the descriptor2910, 2912. For example, principal component analysis may be used toselect and retain a portion of the initial data vector that is mostuseful for effective person re-identification.

In contrast to the image-based descriptors 3022, model-based descriptors3024 are generally determined using a predefined model, such as anartificial neural network. For example, a model-based descriptor 3024may be the output (e.g., a scalar value or vector) output by anartificial neural network trained to recognize people based on theircorresponding representation 3004 a and/or contour 3004 b in top-viewimage 3002. For example, a Siamese neural network may be trained toassociate representations 3004 a and/or contours 3004 b in top-viewimages 3002 with corresponding identifiers 2908 and subsequentlyemployed for re-identification 2929.

Returning to FIG. 29, the descriptor comparator 2914 of the trackingsubsystem 2900 may be used to compare the measured descriptor 2912 tocorresponding predetermined descriptors 2910 in order to determine thecorrect identity of a person being tracked. For example, the measureddescriptor 2912 may be compared to a corresponding predetermineddescriptor 2910 in order to determine the correct identifier 2908 forthe person at position 2902. For instance, if the measured descriptor2912 is a height descriptor 3012, it may be compared to predeterminedheight descriptors 2910 for identifiers 2908, or a subset of theidentifiers 2908 determined using the candidate list 2906. Comparing thedescriptors 2910, 2912 may involve calculating a difference betweenscalar descriptor values (e.g., a difference in heights 3012, volumes3018, etc.), determining whether a value of a measured descriptor 2912is within a threshold range of the corresponding predetermineddescriptor 2910 (e.g., determining if a color value 3018 of the measureddescriptor 2912 is within a threshold range of the color value 3018 ofthe predetermined descriptor 2910), determining a cosine similarityvalue between vectors of the measured descriptor 2912 and thecorresponding predetermined descriptor 2910 (e.g., determining a cosinesimilarity value between a measured vector calculated using a textureoperator or neural network and a predetermined vector calculated in thesame manner). In some embodiments, only a subset of the predetermineddescriptors 2910 are compared to the measured descriptor 2912. Thesubset may be selected using the candidate list 2906 for the person atposition 2902 that is being re-identified. For example, the person'scandidate list 2906 may indicate that only a subset (e.g., two, three,or so) of a larger number of identifiers 2908 are likely to beassociated with the tracked object position 2902 that requiresre-identification.

When the correct identifier 2908 is determined by the descriptorcomparator 2914, the comparator 2914 may update the candidate list 2906for the person being re-identified at position 2902 (e.g., by sendingupdate 2916). In some cases, a descriptor 2912 may be measured for anobject that does not require re-identification (e.g., a person for whichthe candidate list 2906 indicates there is 100% probability that theperson corresponds to a single identifier 2908). In these cases,measured identifiers 2912 may be used to update and/or maintain thepredetermined descriptors 2910 for the person's known identifier 2908(e.g., by sending update 2918). For instance, a predetermined descriptor2910 may need to be updated if a person associated with the position2902 has a change of appearance while moving through the space 102(e.g., by adding or removing an article of clothing, by assuming adifferent posture, etc.).

FIG. 31A illustrates positions over a period of time of tracked people3102, 3104, 3106, during an example operation of tracking system 2900.The first person 3102 has a corresponding trajectory 3108 represented bythe solid line in FIG. 31A. Trajectory 3108 corresponds to the historyof positions of person 3102 in the space 102 during the period of time.Similarly, the second person 3104 has a corresponding trajectory 3110represented by the dashed-dotted line in FIG. 31A. Trajectory 3110corresponds to the history of positions of person 3104 in the space 102during the period of time. The third person 3106 has a correspondingtrajectory 3112 represented by the dotted line in FIG. 31A. Trajectory3112 corresponds to the history of positions of person 3112 in the space102 during the period of time.

When each of the people 3102, 3104, 3106 first enter the space 102(e.g., when they are within region 3114), predetermined descriptors 2910are generally determined for the people 3102, 3104, 3106 and associatedwith the identifiers 2908 of the people 3102, 3104, 3106. Thepredetermined descriptors 2910 are generally accessed when the identityof one or more of the people 3102, 3104, 3106 is not sufficientlycertain (e.g., based on the corresponding candidate list 2906 and/or inresponse to a “collision event,” as described below) in order tore-identify the person 3102, 3104, 3106. For example, re-identificationmay be needed following a “collision event” between two or more of thepeople 3102, 3104, 3106. A collision event typically corresponds to animage frame in which contours associated with different people merge toform a single contour (e.g., the detection of merged contour 2220 shownin FIG. 22 may correspond to detecting a collision event). In someembodiments, a collision event corresponds to a person being locatedwithin a threshold distance of another person (see, e.g., distance 2718a and 2722 in FIG. 27 and the corresponding description above). Moregenerally, a collision event may correspond to any event that results ina person's candidate list 2906 indicating that re-identification isneeded (e.g., based on probabilities stored in the candidate list2906—see FIGS. 27-28 and the corresponding description above). In theexample of FIG. 31A, when the people 3102, 3104, 3106 are within region3114, the tracking subsystem 2900 may determine a first heightdescriptor 3012 associated with a first height of the first person 3102,a first contour descriptor 3014 associated with a shape of the firstperson 3102, a first anchor descriptor 3024 corresponding to a firstvector generated by an artificial neural network for the first person3102, and/or any other descriptors 2910 described with respect to FIG.30 above. Each of these descriptors is stored for use as a predetermineddescriptor 2910 for re-identifying the first person 3102. Thesepredetermined descriptors 2910 are associated with the first identifier(i.e., of identifiers 2908) of the first person 3102. When the identityof the first person 3102 is certain (e.g., prior to the first collisionevent at position 3116), each of the descriptors 2910 described abovemay be determined again to update the predetermined descriptors 2910.For example, if person 3102 moves to a position in the space 102 thatallows the person 3102 to be within a desired region-of-interest (e.g.,region-of-interest 3006 of FIG. 30), new descriptors 2912 may bedetermined. The tracking subsystem 2900 may use these new descriptors2912 to update the previously determined descriptors 2910 (e.g., seeupdate 2918 of FIG. 29). By intermittently updating the predetermineddescriptors 2910, changes in the appearance of people being tracked canbe accounted for (e.g., if a person puts on or removes an article ofclothing, assumes a different posture, etc.).

At a first timestamp associated with a time t₁, the tracking subsystem2900 detects a collision event between the first person 3102 and thirdperson 3106 at position 3116 illustrated in FIG. 31A. For example, thecollision event may correspond to a first tracked position of the firstperson 3102 being within a threshold distance of a second trackedposition of the third person 3106 at the first timestamp. In someembodiments, the collision event corresponds to a first contourassociated with the first person 3102 merging with a third contourassociated with the third person 3106 at the first timestamp. Moregenerally, the collision event may be associated with any occurrencewhich causes a highest value probability of a candidate list associatedwith the first person 3102 and/or the third person 3106 to fall below athreshold value (e.g., as described above with respect to view 2728 ofFIG. 27). In other words, any event causing the identity of person 3102to become uncertain may be considered a collision event.

After the collision event is detected, the tracking subsystem 2900receives a top-view image (e.g., top-view image 3002 of FIG. 30) fromsensor 108. The tracking subsystem 2900 determines, based on thetop-view image, a first descriptor for the first person 3102. Asdescribed above, the first descriptor includes at least one valueassociated with an observable, or calculated, characteristic of thefirst person 3104 (e.g., of representation 3004 a and/or contour 3004 bof FIG. 30). In some embodiments, the first descriptor may be a“lower-cost” descriptor that requires relative few processing resourcesto determine, as described above. For example, the tracking subsystem2900 may be able to determine a lower-cost descriptor more efficientlythan it can determine a higher-cost descriptor (e.g., a model-baseddescriptor 3024 described above with respect to FIG. 30). For instance,a first number of processing cores used to determine the firstdescriptor may be less than a second number of processing cores used todetermine a model-based descriptor 3024 (e.g., using an artificialneural network). Thus, it may be beneficial to re-identify a person,whenever possible, using a lower-cost descriptor whenever possible.

However, in some cases, the first descriptor may not be sufficient forre-identifying the first person 3102. For example, if the first person3102 and the third person 3106 correspond to people with similarheights, a height descriptor 3012 generally cannot be used todistinguish between the people 3102, 3106. Accordingly, before the firstdescriptor 2912 is used to re-identify the first person 3102, thetracking subsystem 2900 may determine whether certain criteria aresatisfied for distinguishing the first person 3102 from the third person3106 based on the first descriptor 2912. In some embodiments, thecriteria are not satisfied when a difference, determined during a timeinterval associated with the collision event (e.g., at a time at or neartime t₁), between the descriptor 2912 of the first person 3102 and acorresponding descriptor 2912 of the third person 3106 is less than aminimum value.

FIG. 31B illustrates the evaluation of these criteria based on thehistory of descriptor values for people 3102 and 3106 over time. Plot3150, shown in FIG. 31B, shows a first descriptor value 3152 for thefirst person 3102 over time and a second descriptor value 3154 for thethird person 3106 over time. In general, descriptor values may fluctuateover time because of changes in the environment, the orientation ofpeople relative to sensors 108, sensor variability, changes inappearance, etc. The descriptor values 3152, 3154 may be associated witha shape descriptor 3014, a volume 3016, a contour-based descriptor 3022,or the like, as described above with respect to FIG. 30. At time t₁, thedescriptor values 3152, 3154 have a relatively large difference 3156that is greater than the threshold difference 3160, illustrated in FIG.31B. Accordingly, in this example, at or near (e.g., within a brief timeinterval of a few seconds or minutes following t₁), the criteria aresatisfied and the descriptor 2912 associated with descriptor values3152, 3154 can generally be used to re-identify the first and thirdpeople 3102, 3106.

When the criteria are satisfied for distinguishing the first person 3102from the third person 3106 based on the first descriptor 2912 (as is thecase at t₁), the descriptor comparator 2914 may compare the firstdescriptor 2912 for the first person 3102 to each of the correspondingpredetermined descriptors 2910 (i.e., for all identifiers 2908).However, in some embodiments, comparator 2914 may compare the firstdescriptor 2912 for the first person 3102 to predetermined descriptors2910 for only a select subset of the identifiers 2908. The subset may beselected using the candidate list 2906 for the person that is beingre-identified (see, e.g., step 3208 of method 3200 described below withrespect to FIG. 32). For example, the person's candidate list 2906 mayindicate that only a subset (e.g., two, three, or so) of a larger numberof identifiers 2908 are likely to be associated with the tracked objectposition 2902 that requires re-identification. Based on this comparison,the tracking subsystem 2900 may identify the predetermined descriptor2910 that is most similar to the first descriptor 2912. For example, thetracking subsystem 2900 may determine that a first identifier 2908corresponds to the first person 3102 by, for each member of the set (orthe determined subset) of the predetermined descriptors 2910,calculating an absolute value of a difference in a value of the firstdescriptor 2912 and a value of the predetermined descriptor 2910. Thefirst identifier 2908 may be selected as the identifier 2908 associatedwith the smallest absolute value.

Referring again to FIG. 31A, at time t₂, a second collision event occursat position 3118 between people 3102, 3106. Turning back to FIG. 31B,the descriptor values 3152, 3154 have a relatively small difference 3158at time t₂ (e.g., compared to difference 3156 at time t₁), which is lessthan the threshold value 3160. Thus, at time t₂, the descriptor 2912associated with descriptor values 3152, 3154 generally cannot be used tore-identify the first and third people 3102, 3106, and the criteria forusing the first descriptor 2912 are not satisfied. Instead, a different,and likely a “higher-cost” descriptor 2912 (e.g., a model-baseddescriptor 3024) should be used to re-identify the first and thirdpeople 3102, 3106 at time t₂.

For example, when the criteria are not satisfied for distinguishing thefirst person 3102 from the third person 3106 based on the firstdescriptor 2912 (as is the case in this example at time t₂), thetracking subsystem 2900 determines a new descriptor 2912 for the firstperson 3102. The new descriptor 2912 is typically a value or vectorgenerated by an artificial neural network configured to identify peoplein top-view images (e.g., a model-based descriptor 3024 of FIG. 30). Thetracking subsystem 2900 may determine, based on the new descriptor 2912,that a first identifier 2908 from the predetermined identifiers 2908 (ora subset determined based on the candidate list 2906, as describedabove) corresponds to the first person 3102. For example, the trackingsubsystem 2900 may determine that the first identifier 2908 correspondsto the first person 3102 by, for each member of the set (or subset) ofpredetermined identifiers 2908, calculating an absolute value of adifference in a value of the first identifier 2908 and a value of thepredetermined descriptors 2910. The first identifier 2908 may beselected as the identifier 2908 associated with the smallest absolutevalue.

In cases where the second descriptor 2912 cannot be used to reliablyre-identify the first person 3102 using the approach described above,the tracking subsystem 2900 may determine a measured descriptor 2912 forall of the “candidate identifiers” of the first person 3102. Thecandidate identifiers generally refer to the identifiers 2908 of people(e.g., or other tracked objects) that are known to be associated withidentifiers 2908 appearing in the candidate list 2906 of the firstperson 3102 (e.g., as described above with respect to FIGS. 27 and 28).For instance, the candidate identifiers may be identifiers 2908 oftracked people (i.e., at tracked object positions 2902) that appear inthe candidate list 2906 of the person being re-identified. FIG. 31Cillustrates how predetermined descriptors 3162, 3164, 3166 for a first,second, and third identifier 2908 may be compared to each of themeasured descriptors 3168, 3170, 3172 for people 3102, 3104, 3106. Thecomparison may involve calculating a cosine similarity value between avectors associated with the descriptors. Based on the results of thecomparison, each person 3102, 3104, 3106 is assigned the identifier 2908corresponding to the best-matching predetermined descriptor 3162, 3164,3166. A best matching descriptor may correspond to a highest cosinesimilarity value (i.e., nearest to one).

FIG. 32 illustrates a method 3200 for re-identifying tracked peopleusing tracking subsystem 2900 illustrated in FIG. 29 and describedabove. The method 3200 may begin at step 3202 where the trackingsubsystem 2900 receives top-view image frames from one or more sensors108. At step 3204, the tracking subsystem 2900 tracks a first person3102 and one or more other people (e.g., people 3104, 3106) in the space102 using at least a portion of the top-view images generated by thesensors 108. For instance, tracking may be performed as described abovewith respect to FIGS. 24-26, or using any appropriate object trackingalgorithm. The tracking subsystem 2900 may periodically determineupdated predetermined descriptors associated with the identifiers 2908(e.g., as described with respect to update 2918 of FIG. 29). In someembodiments, the tracking subsystem 2900, in response to determining theupdated descriptors, determines that one or more of the updatedpredetermined descriptors is different by at least a threshold amountfrom a corresponding previously predetermined descriptor 2910. In thiscase, the tracking subsystem 2900 may save both the updated descriptorand the corresponding previously predetermined descriptor 2910. This mayallow for improved re-identification when characteristics of the peoplebeing tracked may change intermittently during tracking.

At step 3206, the tracking subsystem 2900 determines whetherre-identification of the first tracked person 3102 is needed. This maybe based on a determination that contours have merged in an image frame(e.g., as illustrated by merged contour 2220 of FIG. 22) or on adetermination that a first person 3102 and a second person 3104 arewithin a threshold distance (e.g., distance 2918 b of FIG. 29) of eachother, as described above. In some embodiments, a candidate list 2906may be used to determine that re-identification of the first person 3102is required. For instance, if a highest probability from the candidatelist 2906 associated with the tracked person 3102 is less than athreshold value (e.g., 70%), re-identification may be needed (see alsoFIGS. 27-28 and the corresponding description above). Ifre-identification is not needed, the tracking subsystem 2900 generallycontinues to track people in the space (e.g., by returning to step3204).

If the tracking subsystem 2900 determines at step 3206 thatre-identification of the first tracked person 3102 is needed, thetracking subsystem 2900 may determine candidate identifiers for thefirst tracked person 3102 at step 3208. The candidate identifiersgenerally include a subset of all of the identifiers 2908 associatedwith tracked people in the space 102, and the candidate identifiers maybe determined based on the candidate list 2906 for the first trackedperson 3102. In other words, the candidate identifiers are a subset ofthe identifiers 2906 which are most likely to include the correctidentifier 2908 for the first tracked person 3102 based on a history ofmovements of the first tracked person 3102 and interactions of the firsttracked person 3102 with the one or more other tracked people 3104, 3106in the space 102 (e.g., based on the candidate list 2906 that is updatedin response to these movements and interactions).

At step 3210, the tracking subsystem 2900 determines a first descriptor2912 for the first tracked person 3102. For example, the trackingsubsystem 2900 may receive, from a first sensor 108, a first top-viewimage of the first person 3102 (e.g., such as image 3002 of FIG. 30).For instance, as illustrated in the example of FIG. 30, in someembodiments, the image 3002 used to determine the descriptor 2912includes the representation 3004 a of the object within aregion-of-interest 3006 within the full frame of the image 3002. Thismay provide for more reliable descriptor 2912 determination. In someembodiments, the image data 2904 include depth data (i.e., image data atdifferent depths). In such embodiments, the tracking subsystem 2900 maydetermine the descriptor 2912 based on a depth region-of-interest, wherethe depth region-of-interest corresponds to depths in the imageassociated with the head of person 3102. In these embodiments,descriptors 2912 may be determined that are associated withcharacteristics or features of the head of the person 3102.

At step 3212, the tracking subsystem 2900 may determine whether thefirst descriptor 2912 can be used to distinguish the first person 3102from the candidate identifiers (e.g., one or both of people 3104, 3106)by, for example, determining whether certain criteria are satisfied fordistinguishing the first person 3102 from the candidates based on thefirst descriptor 2912. In some embodiments, the criteria are notsatisfied when a difference, determined during a time intervalassociated with the collision event, between the first descriptor 2912and corresponding descriptors 2910 of the candidates is less than aminimum value, as described in greater detail above with respect toFIGS. 31A,B.

If the first descriptor can be used to distinguish the first person 3102from the candidates (e.g., as was the case at time t₁ in the example ofFIG. 31A,B), the method 3200 proceeds to step 3214 at which point thetracking subsystem 2900 determines an updated identifier for the firstperson 3102 based on the first descriptor 2912. For example, thetracking subsystem 2900 may compare (e.g., using comparator 2914) thefirst descriptor 2912 to the set of predetermined descriptors 2910 thatare associated with the candidate objects determined for the firstperson 3102 at step 3208. In some embodiments, the first descriptor 2912is a data vector associated with characteristics of the first person inthe image (e.g., a vector determined using a texture operator such asthe LBPH algorithm), and each of the predetermined descriptors 2910includes a corresponding predetermined data vector (e.g., determined foreach tracked pers 3102, 3104, 3106 upon entering the space 102). In suchembodiments, the tracking subsystem 2900 compares the first descriptor2912 to each of the predetermined descriptors 2910 associated with thecandidate objects by calculating a cosine similarity value between thefirst data vector and each of the predetermined data vectors. Thetracking subsystem 2900 determines the updated identifier as theidentifier 2908 of the candidate object with the cosine similarity valuenearest one (i.e., the vector that is most “similar” to the vector ofthe first descriptor 2912).

At step 3216, the identifiers 2908 of the other tracked people 3104,3106 may be updated as appropriate by updating other people's candidatelists 2906. For example, if the first tracked person 3102 was found tobe associated with an identifier 2908 that was previously associatedwith the second tracked person 3104. Steps 3208 to 3214 may be repeatedfor the second person 3104 to determine the correct identifier 2908 forthe second person 3104. In some embodiments, when the identifier 2908for the first person 3102 is updated, the identifiers 2908 for people(e.g., one or both of people 3104 and 3106) that are associated with thefirst person's candidate list 2906 are also updated at step 3216. As anexample, the candidate list 2906 of the first person 3102 may have anon-zero probability that the first person 3102 is associated with asecond identifier 2908 originally linked to the second person 3104 and athird probability that the first person 3102 is associated with a thirdidentifier 2908 originally linked to the third person 3106. In thiscase, after the identifier 2908 of the first person 3102 is updated, theidentifiers 2908 of the second and third people 3104, 3106 may also beupdated according to steps 3208-3214.

If, at step 3212, the first descriptor 2912 cannot be used todistinguish the first person 3102 from the candidates (e.g., as was thecase at time t₂ in the example of FIG. 31A,B), the method 3200 proceedsto step 3218 to determine a second descriptor 2912 for the first person3102. As described above, the second descriptor 2912 may be a“higher-level” descriptor such as a model-based descriptor 3024 of FIG.30). For example, the second descriptor 2912 may be less efficient(e.g., in terms of processing resources required) to determine than thefirst descriptor 2912. However, the second descriptor 2912 may be moreeffective and reliable, in some cases, for distinguishing betweentracked people.

At step 3220, the tracking system 2900 determines whether the seconddescriptor 2912 can be used to distinguish the first person 3102 fromthe candidates (from step 3218) using the same or a similar approach tothat described above with respect to step 3212. For example, thetracking subsystem 2900 may determine if the cosine similarity valuesbetween the second descriptor 2912 and the predetermined descriptors2910 are greater than a threshold cosine similarity value (e.g., of0.5). If the cosine similarity value is greater than the threshold, thesecond descriptor 2912 generally can be used.

If the second descriptor 2912 can be used to distinguish the firstperson 3102 from the candidates, the tracking subsystem 2900 proceeds tostep 3222, and the tracking subsystem 2900 determines the identifier2908 for the first person 3102 based on the second descriptor 2912 andupdates the candidate list 2906 for the first person 3102 accordingly.The identifier 2908 for the first person 3102 may be determined asdescribed above with respect to step 3214 (e.g., by calculating a cosinesimilarity value between a vector corresponding to the first descriptor2912 and previously determined vectors associated with the predetermineddescriptors 2910). The tracking subsystem 2900 then proceeds to step3216 described above to update identifiers 2908 (i.e., via candidatelists 2906) of other tracked people 3104, 3106 as appropriate.

Otherwise, if the second descriptor 2912 cannot be used to distinguishthe first person 3102 from the candidates, the tracking subsystem 2900proceeds to step 3224, and the tracking subsystem 2900 determines adescriptor 2912 for all of the first person 3102 and all of thecandidates. In other words, a measured descriptor 2912 is determined forall people associated with the identifiers 2908 appearing in thecandidate list 2906 of the first person 3102 (e.g., as described abovewith respect to FIG. 31C). At step 3226, the tracking subsystem 2900compares the second descriptor 2912 to predetermined descriptors 2910associated with all people related to the candidate list 2906 of thefirst person 3102. For instance, the tracking subsystem 2900 maydetermine a second cosine similarity value between a second data vectordetermined using an artificial neural network and each correspondingvector from the predetermined descriptor values 2910 for the candidates(e.g., as illustrated in FIG. 31C, described above). The trackingsubsystem 2900 then proceeds to step 3228 to determine and update theidentifiers 2908 of all candidates based on the comparison at step 3226before continuing to track people 3102, 3104, 3106 in the space 102(e.g., by returning to step 3204).

Modifications, additions, or omissions may be made to method 3200depicted in FIG. 32. Method 3200 may include more, fewer, or othersteps. For example, steps may be performed in parallel or in anysuitable order. While at times discussed as tracking system 2900 (e.g.,by server 106 and/or client(s) 105) or components thereof performingsteps, any suitable system or components of the system may perform oneor more steps of the method 3200.

Action Detection for Assigning Items to the Correct Person

As described above with respect to FIGS. 12-15 when a weight event isdetected at a rack 112, the item associated with the activated weightsensor 110 may be assigned to the person nearest the rack 112. However,in some cases, two or more people may be near the rack 112 and it maynot be clear who picked up the item. Accordingly, further action may berequired to properly assign the item to the correct person.

In some embodiments, a cascade of algorithms (e.g., from more simpleapproaches based on relatively straightforwardly determined imagefeatures to more complex strategies involving artificial neuralnetworks) may be employed to assign an item to the correct person. Thecascade may be triggered, for example, by (i) the proximity of two ormore people to the rack 112, (ii) a hand crossing into the zone (or a“virtual curtain”) adjacent to the rack (e.g., see zone 3324 of FIG. 33Band corresponding description below) and/or, (iii) a weight signalindicating an item was removed from the rack 112. When it is initiallyuncertain who picked up an item, a unique contour-based approach may beused to assign an item to the correct person. For instance, if twopeople may be reaching into a rack 112 to pick up an item, a contour maybe “dilated” from a head height to a lower height in order to determinewhich person's arm reached into the rack 112 to pick up the item.However, if the results of this efficient contour-based approach do notsatisfy certain confidence criteria, a more computationally expensiveapproach (e.g., involving neural network-based pose estimation) may beused. In some embodiments, the tacking system 100, upon detecting thatmore than one person may have picked up an item, may store a set ofbuffer frames that are most likely to contain useful information foreffectively assigning the item to the correct person. For instance, thestored buffer frames may correspond to brief time intervals when aportion of a person enters the zone adjacent to a rack 112 (e.g., zone3324 of FIG. 33B, described above) and/or when the person exits thiszone.

However, in some cases, it may still be difficult or impossible toassign an item to a person even using more advance artificial neuralnetwork-based pose estimation techniques. In these cases, the trackingsystem 100 may store further buffer frames in order to track the itemthrough the space 102 after it exits the rack 112. When the item comesto a stopped position (e.g., with a sufficiently low velocity), thetracking system 100 determines which person is closer to the stoppeditem, and the item is generally assigned to the nearest person. Thisprocess may be repeated until the item is confidently assigned to thecorrect person.

FIG. 33A illustrates an example scenario in which a first person 3302and a second person 3304 are near a rack 112 storing items 3306 a-c.Each item 3306 a-c is stored on corresponding weight sensors 110 a-c. Asensor 108, which is communicatively coupled to the tracking subsystem3300 (i.e., to the server 106 and/or client(s) 105), generates atop-view depth image 3308 for a field-of-view 3310 which includes therack 112 and people 3302, 3304. The top-view depth image 3308 includes arepresentation 112 a of the rack 112 and representations 3302 a, 3304 aof the first and second people 3302, 3304, respectively. The rack 112(e.g., or its representation 112 a) may be divided into three zones 3312a-c which correspond to the locations of weight sensors 110 a-c and theassociated items 3306 a-c, respectively.

In this example scenario, one of the people 3302, 3304 picks up an item3306 c from weight sensor 110 c, and tracking subsystem 3300 receives atrigger signal 3314 indicating an item 3306 c has been removed from therack 112. The tracking subsystem 3300 includes the client(s) 105 andserver 106 described above with respect to FIG. 1. The trigger signal3314 may indicate the change in weight caused by the item 3306 c beingremoved from sensor 110 c. After receiving the signal 3314, the server106 accesses the top-view image 3308, which may correspond to a time at,just prior to, and/or just following the time the trigger signal 3314was received. In some embodiments, the trigger signal 3314 may also oralternatively be associated with the tracking system 100 detecting aperson 3302, 3304 entering a zone adjacent to the rack (e.g., asdescribed with respect to the “virtual curtain” of FIGS. 12-15 aboveand/or zone 3324 described in greater detail below) to determine towhich person 3302, 3304 the item 3306 c should be assigned. Sincerepresentations 3302 a and 3304 a indicate that both people 3302, 3304are near the rack 112, further analysis is required to assign item 3306c to the correct person 3302, 3304. Initially, the tracking system 100may determine if an arm of either person 3302 or 3304 may be reachingtoward zone 3312 c to pick up item 3306 c. However, as shown in regions3316 and 3318 in image 3308, a portion of both representations 3302 a,3304 a appears to possibly be reaching toward the item 3306 c in zone3312 c. Thus, further analysis is required to determine whether thefirst person 3302 or the second person 3304 picked up item 3306 c.

Following the initial inability to confidently assign item 3306 c to thecorrect person 3302, 3304, the tracking system 100 may use acontour-dilation approach to determine whether person 3302 or 3304picked up item 3306 c. FIG. 33B illustrates implementation of acontour-dilation approach to assigning item 3306 c to the correct person3302 or 3304. In general, contour dilation involves iterative dilationof a first contour associated with the first person 3302 and a secondcontour associated with the second person 3304 from a first smallerdepth to a second larger depth. The dilated contour that crosses intothe zone 3324 adjacent to the rack 112 first may correspond to theperson 3302, 3304 that picked up the item 3306 c. Dilated contours mayneed to satisfy certain criteria to ensure that the results of thecontour-dilation approach should be used for item assignment. Forexample, the criteria may include a requirement that a portion of acontour entering the zone 3324 adjacent to the rack 112 is associatedwith either the first person 3302 or the second person 3304 within amaximum number of iterative dilations, as is described in greater detailwith respect to the contour-detection views 3320, 3326, 3328, and 3332shown in FIG. 33B. If these criteria are not satisfied, another methodshould be used to determine which person 3302 or 3304 picked up item3306 c.

FIG. 33B shows a view 3320, which includes a contour 3302 b detected ata first depth in the top-view image 3308. The first depth may correspondto an approximate head height of a typical person 3322 expected to betracked in the space 102, as illustrated in FIG. 33B. Contour 3302 bdoes not enter or contact the zone 3324 which corresponds to thelocation of a space adjacent to the front of the rack 112 (e.g., asdescribed with respect to the “virtual curtain” of FIGS. 12-15 above).Therefore, the tracking system 100 proceeds to a second depth in image3308 and detects contours 3302 c and 3304 b shown in view 3326. Thesecond depth is greater than the first depth of view 3320. Since neitherof the contours 3302 c or 3304 b enter zone 3324, the tracking system100 proceeds to a third depth in the image 3308 and detects contours3302 d and 3304 c, as shown in view 3328. The third depth is greaterthan the second depth, as illustrated with respect to person 3322 inFIG. 33B.

In view 3328, contour 3302 d appears to enter or touch the edge of zone3324. Accordingly, the tracking system 100 may determine that the firstperson 3302, who is associated with contour 3302 d, should be assignedthe item 3306 c. In some embodiments, after initially assigning the item3306 c to person 3302, the tracking system 100 may project an “armsegment” 3330 to determine whether the arm segment 3330 enters theappropriate zone 3312 c that is associated with item 3306 c. The armsegment 3330 generally corresponds to the expected position of theperson's extended arm in the space occluded from view by the rack 112.If the location of the projected arm segment 3330 does not correspondwith an expected location of item 3306 c (e.g., a location within zone3312 c), the item is not assigned to (or is unassigned from) the firstperson 3302.

Another view 3332 at a further increased fourth depth shows a contour3302 e and contour 3304 d. Each of these contours 3302 e and 3304 dappear to enter or touch the edge of zone 3324. However, since thedilated contours associated with the first person 3302 (reflected incontours 3302 b-e) entered or touched zone 3324 within fewer iterations(or at a smaller depth) than did the dilated contours associated withthe second person 3304 (reflected in contours 3304 b-d), the item 3306 cis generally assigned to the first person 3302. In general, in order forthe item 3306 c to be assigned to one of the people 3302, 3304 usingcontour dilation, a contour may need to enter zone 3324 within a maximumnumber of dilations (e.g., or before a maximum depth is reached). Forexample, if the item 3306 c was not assigned by the fourth depth, thetracking system 100 may have ended the contour-dilation method and movedon to another approach to assigning the item 3306 c, as described below.

In some embodiments the contour-dilation approach illustrated in FIG.33B fails to correctly assign item 3306 c to the correct person 3302,3304. For example, the criteria described above may not be satisfied(e.g., a maximum depth or number of iterations may be exceeded) ordilated contours associated with the different people 3302 or 3304 maymerge, rendering the results of contour-dilation unusable. In suchcases, the tracking system 100 may employ another strategy to determinewhich person 3302, 3304 c picked up item 3306 c. For example, thetracking system 100 may use a pose estimation algorithm to determine apose of each person 3302, 3304.

FIG. 33C illustrates an example output of a pose-estimation algorithmwhich includes a first “skeleton” 3302 f for the first person 3302 and asecond “skeleton” 3304 e for the second person 3304. In this example,the first skeleton 3302 f may be assigned a “reaching pose” because anarm of the skeleton appears to be reaching outward. This reaching posemay indicate that the person 3302 is reaching to pick up item 3306 c. Incontrast, the second skeleton 3304 e does not appear to be reaching topick up item 3306 c. Since only the first skeleton 3302 f appears to bereaching for the item 3306 c, the tracking system 100 may assign theitem 3306 c to the first person 3302. If the results of pose estimationwere uncertain (e.g., if both or neither of the skeletons 3302 f, 3304 eappeared to be reaching for item 3306 c), a different method of itemassignment may be implemented by the tracking system 100 (e.g., bytracking the item 3306 c through the space 102, as described below withrespect to FIGS. 36-37).

FIG. 34 illustrates a method 3400 for assigning an item 3306 c to aperson 3302 or 3304 using tracking system 100. The method 3400 may beginat step 3402 where the tracking system 100 receives an image feedcomprising frames of top-view images generated by the sensor 108 andweight measurements from weight sensors 110 a-c.

At step 3404, the tracking system 100 detects an event associated withpicking up an item 33106 c. In general, the event may be based on aportion of a person 3302, 3304 entering the zone adjacent to the rack112 (e.g., zone 3324 of FIG. 33B) and/or a change of weight associatedwith the item 33106 c being removed from the corresponding weight sensor110 c.

At step 3406, in response to detecting the event at step 3404, thetracking system 100 determines whether more than one person 3302, 3304may be associated with the detected event (e.g., as in the examplescenario illustrated in FIG. 33A, described above). For example, thisdetermination may be based on distances between the people and the rack112, an inter-person distance between the people, a relative orientationbetween the people and the rack 112 (e.g., a person 3302, 3304 notfacing the rack 112 may not be candidate for picking up the item 33106c). If only one person 3302, 3304 may be associated with the event, thatperson 3302, 3304 is associated with the item 3306 c at step 3408. Forexample, the item 3306 c may be assigned to the nearest person 3302,3304, as described with respect to FIGS. 12-14 above.

At step 3410, the item 3306 c is assigned to the person 3302, 3304determined to be associated with the event detected at step 3404. Forexample, the item 3306 c may be added to a digital cart associated withthe person 3302, 3304. Generally, if the action (i.e., picking up theitem 3306 c) was determined to have been performed by the first person3302, the action (and the associated item 3306 c) is assigned to thefirst person 3302, and, if the action was determined to have beenperformed by the second person 3304, the action (and associated item3306 c) is assigned to the second person 3304.

Otherwise, if, at step 3406, more than one person 3302, 3304 may beassociated with the detected event, a select set of buffer frames oftop-view images generated by sensor 108 may be stored at step 3412. Insome embodiments, the stored buffer frames may include only three orfewer frames of top-view images following a triggering event. Thetriggering event may be associated with the person 3302, 3304 enteringthe zone adjacent to the rack 112 (e.g., zone 3324 of FIG. 33B), theportion of the person 3302, 3304 exiting the zone adjacent to the rack112 (e.g., zone 3324 of FIG. 33B), and/or a change in weight determinedby a weight sensor 110 a-c. In some embodiments, the buffer frames mayinclude image frames from the time a change in weight was reported by aweight sensor 110 until the person 3302, 3304 exits the zone adjacent tothe rack 112 (e.g., zone 3324 of FIG. 33B). The buffer frames generallyinclude a subset of all possible frames available from the sensor 108.As such, by storing, and subsequently analyzing, only these storedbuffer frames (or a portion of the stored buffer frames), the trackingsystem 100 may assign actions (e.g., and an associated item 106 a-c) toa correct person 3302, 3304 more efficiently (e.g., in terms of the useof memory and processing resources) than was possible using previoustechnology.

At step 3414, a region-of-interest from the images may be accessed. Forexample, following storing the buffer frames, the tracking system 100may determine a region-of-interest of the top-view images to retain. Forexample, the tracking system 100 may only store a region near the centerof each view (e.g., region 3006 illustrated in FIG. 30 and describedabove).

At step 3416, the tracking system 100 determines, using at least one ofthe buffer frames stored at step 3412 and a first action-detectionalgorithm, whether an action associated with the detected event wasperformed by the first person 3302 or the second person 3304. The firstaction-detection algorithm is generally configured to detect the actionbased on characteristics of one or more contours in the stored bufferframes. As an example, the first action-detection algorithm may be thecontour-dilation algorithm described above with respect to FIG. 33B. Anexample implementation of a contour-based action-detection method isalso described in greater detail below with respect to method 3500illustrated in FIG. 35. In some embodiments, the tracking system 100 maydetermine a subset of the buffer frames to use with the firstaction-detection algorithm. For example, the subset may correspond towhen the person 3302, 3304 enters the zone adjacent to the rack 112(e.g., zone 3324 illustrated in FIG. 33B).

At step 3418, the tracking system 100 determines whether results of thefirst action-detection algorithm satisfy criteria indicating that thefirst algorithm is appropriate for determining which person 3302, 3304is associated with the event (i.e., picking up item 3306 c, in thisexample). For example, for the contour-dilation approach described abovewith respect to FIG. 33B and below with respect to FIG. 35, the criteriamay be a requirement to identify the person 3302, 3304 associated withthe event within a threshold number of dilations (e.g., before reachinga maximum depth). Whether the criteria are satisfied at step 3416 may bebased at least in part on the number of iterations required to implementthe first action-detection algorithm. If the criteria are satisfied atstep 3418, the tracking system 100 proceeds to step 3410 and assigns theitem 3306 c to the person 3302, 3304 associated with the eventdetermined at step 3416.

However, if the criteria are not satisfied at step 3418, the trackingsystem 100 proceeds to step 3420 and uses a different action-detectionalgorithm to determine whether the action associated with the eventdetected at step 3404 was performed by the first person 3302 or thesecond person 3304. This may be performed by applying a secondaction-detection algorithm to at least one of the buffer frames selectedat step 3412. The second action-detection algorithm may be configured todetect the action using an artificial neural network. For example, thesecond algorithm may be a pose estimation algorithm used to determinewhether a pose of the first person 3302 or second person 3304corresponds to the action (e.g., as described above with respect to FIG.33C). In some embodiments, the tracking system 100 may determine asecond subset of the buffer frames to use with the second actiondetection algorithm. For example, the subset may correspond to the timewhen the weight change is reported by the weight sensor 110. The pose ofeach person 3302, 3304 at the time of the weight change may provide agood indication of which person 3302, 3304 picked up the item 3306 c.

At step 3422, the tracking system 100 may determine whether the secondalgorithm satisfies criteria indicating that the second algorithm isappropriate for determining which person 3302, 3304 is associated withthe event (i.e., with picking up item 3306 c). For example, if the poses(e.g., determined from skeletons 3302 f and 3304 e of FIG. 33C,described above) of each person 3302, 3304 still suggest that eitherperson 3302, 3304 could have picked up the item 3306 c, the criteria maynot be satisfied, and the tracking system 100 proceeds to step 3424 toassign the object using another approach (e.g., by tracking movement ofthe item 3306 a-c through the space 102, as described in greater detailbelow with respect to FIGS. 36 and 37).

Modifications, additions, or omissions may be made to method 3400depicted in FIG. 34. Method 3400 may include more, fewer, or othersteps. For example, steps may be performed in parallel or in anysuitable order. While at times discussed as tracking system 100 orcomponents thereof performing steps, any suitable system or componentsof the system may perform one or more steps of the method 3400.

As described above, the first action-detection algorithm of step 3416may involve iterative contour dilation to determine which person 3302,3304 is reaching to pick up an item 3306 a-c from rack 112. FIG. 35illustrates an example method 3500 of contour dilation-based itemassignment. The method 3500 may begin from step 3416 of FIG. 34,described above, and proceed to step 3502. At step 3502, the trackingsystem 100 determines whether a contour is detected at a first depth(e.g., the first depth of FIG. 33B described above). For example, in theexample illustrated in FIG. 33B, contour 3302 b is detected at the firstdepth. If a contour is not detected, the tracking system 100 proceeds tostep 3504 to determine if the maximum depth (e.g., the fourth depth ofFIG. 33B) has been reached. If the maximum depth has not been reached,the tracking system 100 iterates (i.e., moves) to the next depth in theimage at step 3506. Otherwise, if the maximum depth has been reached,method 3500 ends.

If at step 3502, a contour is detected, the tracking system proceeds tostep 3508 and determines whether a portion of the detected contouroverlaps, enters, or otherwise contacts the zone adjacent to the rack112 (e.g., zone 3324 illustrated in FIG. 33B). In some embodiments, thetracking system 100 determines if a projected arm segment (e.g., armsegment 3330 of FIG. 33B) of a contour extends into an appropriate zone3312 a-c of the rack 112. If no portion of the contour extends into thezone adjacent to the rack 112, the tracking system 100 determineswhether the maximum depth has been reached at step 3504. If the maximumdepth has not been reached, the tracking system 100 iterates to the nextlarger depth and returns to step 3502.

At step 3510, the tracking system 100 determines the number ofiterations (i.e., the number of times step 3506 was performed) beforethe contour was determined to have entered the zone adjacent to the rack112 at step 3508. At step 3512, this number of iterations is compared tothe number of iterations for a second (i.e., different) detectedcontour. For example, steps 3502 to 35010 may be repeated to determinethe number of iterations (at step 3506) for the second contour to enterthe zone adjacent to the rack 112. If the number of iterations is lessthan that of the second contour, the item is assigned to the firstperson 3302 at step 3514. Otherwise, the item may be assigned to thesecond person 3304 at step 3516. For example, as described above withrespect to FIG. 33B, the first dilated contours 3302 b-e entered thezone 3324 adjacent to the rack 112 within fewer iterations than did thesecond dilated contours 3304 b. In this example, the item is assigned tothe person 3302 associated with the first contour 3302 b-d.

In some embodiments, a dilated contour (i.e., the contour generated viatwo or more passes through step 3506) must satisfy certain criteria inorder for it to be used for assigning an item. For instance, a contourmay need to enter the zone adjacent to the rack within a maximum numberof dilations (e.g., or before a maximum depth is reached), as describedabove. As another example, a dilated contour may need to include lessthan a threshold number of pixels. If a contour is too large it may be a“merged contour” that is associated with two closely spaced people (seeFIG. 22 and the corresponding description above).

Modifications, additions, or omissions may be made to method 3500depicted in FIG. 35. Method 3500 may include more, fewer, or othersteps. For example, steps may be performed in parallel or in anysuitable order. While at times discussed as tracking system 100 orcomponents thereof performing steps, any suitable system or componentsof the system may perform one or more steps of the method 3500.

Item Tracking-Based Item Assignment

As described above, in some cases, an item 3306 a-c cannot be assignedto the correct person even using a higher-level algorithm such as theartificial neural network-based pose estimation described above withrespect to FIGS. 33C and 34. In these cases, the position of the item3306 c after it exits the rack 112 may be tracked in order to assign theitem 3306 c to the correct person 3302, 3304. In some embodiments, thetracking system 100 does this by tracking the item 3306 c after it exitsthe rack 112, identifying a position where the item stops moving, anddetermining which person 3302, 3304 is nearest to the stopped item 3306c. The nearest person 3302, 3304 is generally assigned the item 3306 c.

FIGS. 36A,B illustrate this item tracking-based approach to itemassignment. FIG. 36A shows a top-view image 3602 generated by a sensor108. FIG. 36B shows a plot 3620 of the item's velocity 3622 over time.As shown in FIG. 36A, image 3602 includes a representation of a person3604 holding an item 3606 which has just exited a zone 3608 adjacent toa rack 112. Since a representation of a second person 3610 may also havebeen associated with picking up the item 3606, item-based tracking isrequired to properly assign the item 3606 to the correct person 3604,3610 (e.g., as described above with respect people 3302, 3304 and item3306 c for FIGS. 33-35). Tracking system 100 may (i) track the positionof the item 3606 over time after the item 3606 exits the rack 112, asillustrated in tracking views 3610 and 3616, and (ii) determine thevelocity of the item 3606, as shown in curve 3622 of plot 3620 in FIG.36B. The velocity 3622 shown in FIG. 36B is zero at the inflectionpoints corresponding to a first stopped time (t_(stopped,1)) and asecond stopped time (t_(stopped,2)). More generally, the time when theitem 3606 is stopped may correspond to a time when the velocity 3622 isless than a threshold velocity 3624.

Tracking view 3612 of FIG. 36A shows the position 3604 a of the firstperson 3604, a position 3606 a of item 3606, and a position 3610 a ofthe second person 3610 at the first stopped time. At the first stoppedtime a (t_(stopped,1)) the positions 3604 a, 3610 a are both near theposition 3606 a of the item 3606. Accordingly, the tracking system 100may not be able to confidently assign item 3606 to the correct person3604 or 3610. Thus, the tracking system 100 continues to track the item3606. Tracking view 3614 shows the position 3604 a of the first person3604, the position 3606 a of the item 3606, and the position 3610 a ofthe second person 3610 at the second stopped time (t_(stopped,2)). Sinceonly the position 3604 a of the first person 3604 is near the position3606 a of the item 3606, the item 3606 is assigned to the first person3604.

More specifically, the tracking system 100 may determine, at eachstopped time, a first distance 3626 between the stopped item 3606 andthe first person 3604 and a second distance 3628 between the stoppeditem 3606 and the second person 3610. Using these distances 3626, 3628,the tracking system 100 determines whether the stopped position of theitem 3606 in the first frame is nearer the first person 3604 or nearerthe second person 3610 and whether the distance 3626, 3628 is less thana threshold distance 3630. At the first stopped time of view 3612, bothdistances 3626, 3628 are less than the threshold distance 3630. Thus,the tracking system 100 cannot reliably determine which person 3604,3610 should be assigned the item 3606. In contrast, at the secondstopped time of view 3614, only the first distance 3626 is less than thethreshold distance 3630. Therefore, the tracking system may assign theitem 3606 to the first person 3604 at the second stopped time.

FIG. 37 illustrates an example method 3700 of assigning an item 3606 toa person 3604 or 3610 based on item tracking using tracking system 100.Method 3700 may begin at step 3424 of method 3400 illustrated in FIG. 34and described above and proceed to step 3702. At step 3702, the trackingsystem 100 may determine that item tracking is needed (e.g., because theaction-detection based approaches described above with respect to FIGS.33-35 were unsuccessful). At step 3504, the tracking system 100 storesand/or accesses buffer frames of top-view images generated by sensor108. The buffer frames generally include frames from a time periodfollowing a portion of the person 3604 or 3610 exiting the zone 3608adjacent to the rack 11236.

At step 3706, the tracking system 100 tracks, in the stored frames, aposition of the item 3606. The position may be a local pixel positionassociated with the sensor 108 (e.g., determined by client 105) or aglobal physical position in the space 102 (e.g., determined by server106 using an appropriate homography). In some embodiments, the item 3606may include a visually observable tag that can be viewed by the sensor108 and detected and tracked by the tracking system 100 using the tag.In some embodiments, the item 3606 may be detected by the trackingsystem 100 using a machine learning algorithm. To facilitate detectionof many item types under a broad range of conditions (e.g., differentorientations relative to the sensor 108, different lighting conditions,etc.), the machine learning algorithm may be trained using syntheticdata (e.g., artificial image data that can be used to train thealgorithm).

At step 3708, the tracking system 100 determines whether a velocity 3622of the item 3606 is less than a threshold velocity 3624. For example,the velocity 3622 may be calculated, based on the tracked position ofthe item 3606. For instance, the distance moved between frames may beused to calculate a velocity 3622 of the item 3606. A particle filtertracker (e.g., as described above with respect to FIGS. 24-26) may beused to calculate item velocity 3622 based on estimated future positionsof the item. If the item velocity 3622 is below the threshold 3624, thetracking system 100 identifies, a frame in which the velocity 3622 ofthe item 3606 is less than the threshold velocity 3624 and proceeds tostep 3710. Otherwise, the tracking system 100 continues to track theitem 3606 at step 3706.

At step 3710, the tracking system 100 determines, in the identifiedframe, a first distance 3626 between the stopped item 3606 and a firstperson 3604 and a second distance 3628 between the stopped item 3606 anda second person 3610. Using these distances 3626, 3628, the trackingsystem 100 determines, at step 3712, whether the stopped position of theitem 3606 in the first frame is nearer the first person 3604 or nearerthe second person 3610 and whether the distance 3626, 3628 is less thana threshold distance 3630. In general, in order for the item 3606 to beassigned to the first person 3604, the item 3606 should be within thethreshold distance 3630 from the first person 3604, indicating theperson is likely holding the item 3606, and closer to the first person3604 than to the second person 3610. For example, at step 3712, thetracking system 100 may determine that the stopped position is a firstdistance 3626 away from the first person 3604 and a second distance 3628away from the second person 3610. The tracking system 100 may determinean absolute value of a difference between the first distance 3626 andthe second distance 3628 and may compare the absolute value to athreshold distance 3630. If the absolute value is less than thethreshold distance 3630, the tracking system returns to step 3706 andcontinues tracking the item 3606. Otherwise, the tracking system 100 isgreater than the threshold distance 3630 and the item 3606 issufficiently close to the first person 3604, the tracking systemproceeds to step 3714 and assigns the item 3606 to the first person3604.

Modifications, additions, or omissions may be made to method 3700depicted in FIG. 37. Method 3700 may include more, fewer, or othersteps. For example, steps may be performed in parallel or in anysuitable order. While at times discussed as tracking system 100 orcomponents thereof performing steps, any suitable system or componentsof the system may perform one or more steps of the method 3700.

Hardware Configuration

FIG. 38 is an embodiment of a device 3800 (e.g. a server 106 or a client105) configured to track objects and people within a space 102. Thedevice 3800 comprises a processor 3802, a memory 3804, and a networkinterface 3806. The device 3800 may be configured as shown or in anyother suitable configuration.

The processor 3802 comprises one or more processors operably coupled tothe memory 3804. The processor 3802 is any electronic circuitryincluding, but not limited to, state machines, one or more centralprocessing unit (CPU) chips, logic units, cores (e.g. a multi-coreprocessor), field-programmable gate array (FPGAs), application specificintegrated circuits (ASICs), or digital signal processors (DSPs). Theprocessor 3802 may be a programmable logic device, a microcontroller, amicroprocessor, or any suitable combination of the preceding. Theprocessor 3802 is communicatively coupled to and in signal communicationwith the memory 3804. The one or more processors are configured toprocess data and may be implemented in hardware or software. Forexample, the processor 3802 may be 8-bit, 16-bit, 32-bit, 64-bit or ofany other suitable architecture. The processor 3802 may include anarithmetic logic unit (ALU) for performing arithmetic and logicoperations, processor registers that supply operands to the ALU andstore the results of ALU operations, and a control unit that fetchesinstructions from memory and executes them by directing the coordinatedoperations of the ALU, registers and other components.

The one or more processors are configured to implement variousinstructions. For example, the one or more processors are configured toexecute instructions to implement a tracking engine 3808. In this way,processor 3802 may be a special purpose computer designed to implementthe functions disclosed herein. In an embodiment, the tracking engine3808 is implemented using logic units, FPGAs, ASICs, DSPs, or any othersuitable hardware. The tracking engine 3808 is configured operate asdescribed in FIGS. 1-18. For example, the tracking engine 3808 may beconfigured to perform the steps of methods 200, 600, 800, 1000, 1200,1500, 1600, and 1700 as described in FIGS. 2, 6, 8, 10, 12, 15, 16, and17, respectively.

The memory 3804 comprises one or more disks, tape drives, or solid-statedrives, and may be used as an over-flow data storage device, to storeprograms when such programs are selected for execution, and to storeinstructions and data that are read during program execution. The memory3804 may be volatile or non-volatile and may comprise read-only memory(ROM), random-access memory (RAM), ternary content-addressable memory(TCAM), dynamic random-access memory (DRAM), and static random-accessmemory (SRAM).

The memory 3804 is operable to store tracking instructions 3810,homographies 118, marker grid information 716, marker dictionaries 718,pixel location information 908, adjacency lists 1114, tracking lists1112, digital carts 1410, item maps 1308, and/or any other data orinstructions. The tracking instructions 3810 may comprise any suitableset of instructions, logic, rules, or code operable to execute thetracking engine 3808.

The homographies 118 are configured as described in FIGS. 2-5B. Themarker grid information 716 is configured as described in FIGS. 6-7. Themarker dictionaries 718 are configured as described in FIGS. 6-7. Thepixel location information 908 is configured as described in FIGS. 8-9.The adjacency lists 1114 are configured as described in FIGS. 10-11. Thetracking lists 1112 are configured as described in FIGS. 10-11. Thedigital carts 1410 are configured as described in FIGS. 12-18. The itemmaps 1308 are configured as described in FIGS. 12-18.

The network interface 3806 is configured to enable wired and/or wirelesscommunications. The network interface 3806 is configured to communicatedata between the device 3800 and other, systems, or domain. For example,the network interface 3806 may comprise a WIFI interface, a LANinterface, a WAN interface, a modem, a switch, or a router. Theprocessor 3802 is configured to send and receive data using the networkinterface 3806. The network interface 3806 may be configured to use anysuitable type of communication protocol as would be appreciated by oneof ordinary skill in the art.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

1. A system, comprising: a plurality of sensors, each sensor configuredto generate top-view images of at least a portion of a space; and atracking subsystem communicatively coupled to the plurality of sensors,the tracking subsystem configured to: track, over a period of time usingtop-view images generated by at least one of the plurality of sensors, afirst global position of a first object in the space, based on pixelcoordinates of a first contour associated with the first object; track,over the period of time using top-view images generated by at least oneof the plurality of sensors, a second global position of a second objectin the space, based on pixel coordinates of a second contour associatedwith the second object; at a first time stamp corresponding to a timewithin the period of time, detect a collision event between the firstand second object, wherein the collision event corresponds to the firsttracked position being within a threshold distance of the second trackedposition; after detecting the collision event, receive a first top-viewimage from a first sensor of the plurality of sensors, the firsttop-view image comprising a top-view image of the first object;determine, based on the first top-view image, a first descriptor for thefirst object, the first descriptor comprising at least one valueassociated with an observable characteristic of the first contour;determine that criteria are not satisfied for distinguishing the firstobject from the second object based on the first descriptor, wherein thecriteria are not satisfied when a difference, during a time intervalassociated with the collision event, between a first value of the firstdescriptor and a second value of a second descriptor associated with thesecond object is less than a minimum value; in response to determiningthat the criteria are not satisfied, determine a third descriptor forthe first contour, wherein the third descriptor comprises a valuegenerated by an artificial neural network configured to identify objectsin top-view images; and determine, based on the third descriptor, that afirst identifier from a set of predefined identifiers corresponds to thefirst object.
 2. The system of claim 1, wherein the tracking subsystemis configured to: determine that the criteria are satisfied fordistinguishing the first object from the second object based on thefirst descriptor, wherein the criteria are satisfied when the differencebetween the first value of the first descriptor and the second value ofthe second descriptor is greater than or equal to the minimum value;identify, based on the first descriptor, the first identifier from theset of predefined identifiers; and associate the first identifier withthe first object.
 3. The system if claim 1, wherein the trackingsubsystem is configured to determine that the first identifiercorresponds to the first object by: for each member of the set ofpredefined identifiers, calculating an absolute value of a difference ina value of the first identifier and a value of the predefinedidentifier; and determining the first identifier as the predefinedidentifier associated with the calculated absolute value with a smallestvalue.
 4. The system of claim 1, wherein the tracking subsystem isfurther configured to determine the first descriptor by: calculating aninitial data vector for the first contour using a texture operator; andselect a portion of the initial data vector to include in the firstdescriptor of the first descriptor using principal component analysis.5. The system of claim 1, wherein a first number of processing coresused to determine the first descriptor is less than a second number ofprocessing cores used to determine the third descriptor using theartificial neural network.
 6. The system of claim 1, wherein: the set ofpredefined identifiers comprise the first identifier of the first objectand a second identifier of the second object; and the tracking subsystemis further configured to: during a first initial time period prior tothe period of time: determine a first height descriptor associated witha first height of the first object, a first contour descriptorassociated with a shape of the first contour, and a first anchordescriptor corresponding to a first vector generated by the artificialneural network for the first contour; and associate the first heightdescriptor, first contour descriptor, and first anchor descriptor withthe first identifier; during a second initial time period prior to theperiod of time: determine a second height descriptor associated with asecond height of the second object, a second contour descriptorassociated with a shape of the second contour, and a second anchordescriptor corresponding to a second vector generated by the artificialneural network for the second contour; and associate the second heightdescriptor, second contour descriptor, and second anchor descriptor withthe first identifier.
 7. The system of claim 6, wherein the firstdescriptor comprises a height of the first object; and the trackingsubsystem is further configured to: determine that the criteria aresatisfied for distinguishing the first object from the second objectbased on the first descriptor, wherein the criteria are satisfied whenthe difference between the first value of the first descriptor and thesecond value of the second descriptor is greater than or equal to theminimum value; in response to determining the height is within athreshold range of the first height descriptor, determine that the firstobject is associated with the first descriptor; and in response todetermining the first object is associated with the first descriptor,associate the first object with the first identifier.
 8. The system ofclaim 1, wherein the collision event corresponds to the first contourmerging with the second contour in a first top-view image frame from afirst sensor of the plurality of sensors; and the tracking subsystem isfurther configured to: in response to detecting the collision event,receive the top-view image frames from the first sensor of the pluralityof sensors at least until the first contour and second contour are nolonger merged; and after the first and second contours are no longermerged, determine, using the first object-identification algorithm, thefirst descriptor for the first object.
 9. A method, comprising:tracking, over a period of time using top-view images generated by atleast one of a plurality of sensors, a first global position of a firstobject in the space, based on pixel coordinates of a first contourassociated with the first object, wherein each sensor of the pluralityof sensors is configured to generate top-view images of at least aportion of a space; tracking, over the period of time using top-viewimages generated by at least one of the plurality of sensors, a secondglobal position of a second object in the space, based on pixelcoordinates of a second contour associated with the second object; at afirst time stamp corresponding to a time within the period of time,detecting a collision event between the first and second object, whereinthe collision event corresponds to the first tracked position beingwithin a threshold distance of the second tracked position; afterdetecting the collision event, receiving a first top-view image from afirst sensor of the plurality of sensors, the first top-view imagecomprising a top-view image of the first object; determining, based onthe first top-view image, a first descriptor for the first object, thefirst descriptor comprising at least one value associated with anobservable characteristic of the first contour; determining thatcriteria are not satisfied for distinguishing the first object from thesecond object based on the first descriptor, wherein the criteria arenot satisfied when a difference, during a time interval associated withthe collision event, between a first value of the first descriptor and asecond value of a second descriptor associated with the second object isless than a minimum value; in response to determining that the criteriaare not satisfied, determining a third descriptor for the first contour,wherein the third descriptor comprises a value generated by anartificial neural network configured to identify objects in top-viewimages; and determining, based on the third descriptor, that a firstidentifier from a set of predefined identifiers corresponds to the firstobject.
 10. The method of claim 9, further comprising: determining thatthe criteria are satisfied for distinguishing the first object from thesecond object based on the first descriptor, wherein the criteria aresatisfied when the difference between the first value of the firstdescriptor and the second value of the second descriptor is greater thanor equal to the minimum value; identifying, based on the firstdescriptor, the first identifier from the set of predefined identifiers;and associating the first identifier with the first object.
 11. Themethod if claim 9, further comprising determining that the firstidentifier corresponds to the first object by: for each member of theset of predefined identifiers, calculating an absolute value of adifference in a value of the first identifier and a value of thepredefined identifier; and determining the first identifier as thepredefined identifier associated with the calculated absolute value witha smallest value.
 12. The method of claim 9, further comprisingdetermining the first descriptor by: calculating an initial data vectorfor the first contour using a texture operator; and select a portion ofthe initial data vector to include in the first descriptor of the firstdescriptor using principal component analysis.
 13. The method of claim9, wherein: the set of predefined identifiers comprise the firstidentifier of the first object and a second identifier of the secondobject; and the method further comprises: during a first initial timeperiod prior to the period of time: determining a first heightdescriptor associated with a first height of the first object, a firstcontour descriptor associated with a shape of the first contour, and afirst anchor descriptor corresponding to a first vector generated by theartificial neural network for the first contour; and associating thefirst height descriptor, first contour descriptor, and first anchordescriptor with the first identifier; during a second initial timeperiod prior to the period of time: determining a second heightdescriptor associated with a second height of the second object, asecond contour descriptor associated with a shape of the second contour,and a second anchor descriptor corresponding to a second vectorgenerated by the artificial neural network for the second contour; andassociating the second height descriptor, second contour descriptor, andsecond anchor descriptor with the first identifier.
 14. The method ofclaim 13, wherein the first descriptor comprises a height of the firstobject; and The method further comprises: determining that the criteriaare satisfied for distinguishing the first object from the second objectbased on the first descriptor, wherein the criteria are satisfied whenthe difference between the first value of the first descriptor and thesecond value of the second descriptor is greater than or equal to theminimum value; in response to determining the height is within athreshold range of the first height descriptor, determining that thefirst object is associated with the first descriptor; and in response todetermining the first object is associated with the first descriptor,associating the first object with the first identifier.
 15. The methodof claim 9, wherein the collision event corresponds to the first contourmerging with the second contour in a first top-view image frame from afirst sensor of the plurality of sensors; and the method furthercomprises: in response to detecting the collision event, receiving thetop-view image frames from the first sensor of the plurality of sensorsat least until the first contour and second contour are no longermerged; and after the first and second contours are no longer merged,determining, using the first object-identification algorithm, the firstdescriptor for the first object.
 16. A tracking subsystemcommunicatively coupled to a plurality of sensors, each sensor of theplurality of sensors configured generate top-view images of at least aportion of a space, the tracking subsystem configured to: track, over aperiod of time using top-view images generated by at least one of theplurality of sensors, a first global position of a first object in thespace, based on pixel coordinates of a first contour associated with thefirst object; track, over the period of time using top-view imagesgenerated by at least one of the plurality of sensors, a second globalposition of a second object in the space, based on pixel coordinates ofa second contour associated with the second object; at a first timestamp corresponding to a time within the period of time, detect acollision event between the first and second object, wherein thecollision event corresponds to the first tracked position being within athreshold distance of the second tracked position; after detecting thecollision event, receive a first top-view image from a first sensor ofthe plurality of sensors, the first top-view image comprising a top-viewimage of the first object; determine, based on the first top-view image,a first descriptor for the first object, the first descriptor comprisingat least one value associated with an observable characteristic of thefirst contour; determine that criteria are not satisfied fordistinguishing the first object from the second object based on thefirst descriptor, wherein the criteria are not satisfied when adifference, during a time interval associated with the collision event,between a first value of the first descriptor and a second value of asecond descriptor associated with the second object is less than aminimum value; in response to determining that the criteria are notsatisfied, determine a third descriptor for the first contour, whereinthe third descriptor comprises a value generated by an artificial neuralnetwork configured to identify objects in top-view images; anddetermine, based on the third descriptor, that a first identifier from aset of predefined identifiers corresponds to the first object.
 17. Thetracking subsystem of claim 16, further configured to: determine thatthe criteria are satisfied for distinguishing the first object from thesecond object based on the first descriptor, wherein the criteria aresatisfied when the difference between the first value of the firstdescriptor and the second value of the second descriptor is greater thanor equal to the minimum value; identify, based on the first descriptor,the first identifier from the set of predefined identifiers; andassociate the first identifier with the first object.
 18. The trackingsubsystem of claim 16, further configured to determine that the firstidentifier corresponds to the first object by: for each member of theset of predefined identifiers, calculating an absolute value of adifference in a value of the first identifier and a value of thepredefined identifier; and determining the first identifier as thepredefined identifier associated with the calculated absolute value witha smallest value.
 19. The tracking subsystem of claim 16, furtherconfigured to determine the first descriptor by: calculating an initialdata vector for the first contour using a texture operator; and select aportion of the initial data vector to include in the first descriptor ofthe first descriptor using principal component analysis.
 20. Thetracking subsystem of claim 16, wherein: the set of predefinedidentifiers comprise the first identifier of the first object and asecond identifier of the second object; and the tracking subsystem isfurther configured to: during a first initial time period prior to theperiod of time: determine a first height descriptor associated with afirst height of the first object, a first contour descriptor associatedwith a shape of the first contour, and a first anchor descriptorcorresponding to a first vector generated by the artificial neuralnetwork for the first contour; and associate the first heightdescriptor, first contour descriptor, and first anchor descriptor withthe first identifier; during a second initial time period prior to theperiod of time: determine a second height descriptor associated with asecond height of the second object, a second contour descriptorassociated with a shape of the second contour, and a second anchordescriptor corresponding to a second vector generated by the artificialneural network for the second contour; and associate the second heightdescriptor, second contour descriptor, and second anchor descriptor withthe first identifier.